├── APDSObservation ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── code │ ├── README.md │ └── code_for_using_dataModel.Transportation_APDSObservation.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── AnonymousCommuterId ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_AnonymousCommuterId.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── AnprFlowObserved ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_AnprFlowObserved.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── BikeHireDockingStation ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_BikeHireDockingStation.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── BikeLane ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_BikeLane.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-geojsonfeature.json │ ├── example-geojsonfeature.json.csv │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── CONTRIBUTORS.yaml ├── CityWork ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ └── code_for_using_dataModel.Transportation_CityWork.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── CrowdFlowObserved ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_CrowdFlowObserved.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── EVChargingStation ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_EVChargingStation.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── FareCollectionSystem ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_FareCollectionSystem.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── FleetVehicle ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_FleetVehicle.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── FleetVehicleOperation ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_FleetVehicleOperation.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── FleetVehicleStatus ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_FleetVehicleStatus.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── ItemFlowObserved ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_ItemFlowObserved.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── README.md ├── RestrictedTrafficArea ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_RestrictedTrafficArea.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-geojsonfeature.json │ ├── example-geojsonfeature.json.csv │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── RestrictionException ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_RestrictionException.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── Road ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_Road.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── RoadAccident ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_RoadAccident.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-geojsonfeature.json │ ├── example-geojsonfeature.json.csv │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── RoadSegment ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_RoadSegment.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-geojsonfeature.json │ ├── example-geojsonfeature.json.csv │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── SpecialRestriction ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_SpecialRestriction.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── TrafficFlowObserved ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_TrafficFlowObserved.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── TrafficViolation ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_TrafficViolation.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── TransportStation ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_TransportStation.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── Vehicle ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_Vehicle.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── VehicleFault ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_VehicleFault.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── VehicleModel ├── ADOPTERS.yaml ├── LICENSE.md ├── README.md ├── SmartDataModelBadge.png ├── code │ ├── README.md │ ├── code_for_using_dataModel.Transportation_VehicleModel.py │ └── code_for_using_pydantic.py ├── doc │ ├── spec.md │ ├── spec_DE.md │ ├── spec_ES.md │ ├── spec_FR.md │ ├── spec_IT.md │ ├── spec_JA.md │ ├── spec_KO.md │ └── spec_ZH.md ├── examples │ ├── example-normalized.json │ ├── example-normalized.json.csv │ ├── example-normalized.jsonld │ ├── example-normalized.jsonld.csv │ ├── example.json │ ├── example.json.csv │ ├── example.jsonld │ └── example.jsonld.csv ├── model.yaml ├── notes.yaml ├── schema.json ├── schema.sql ├── schemaDTDL.json └── swagger.yaml ├── context.jsonld ├── context2.jsonld └── notes.yaml /APDSObservation/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model APDSObservation of the Subject Transportation. 2 | currentAdopters: 3 | - 4 | adopter: Davy Van Deun 5 | description: Project manager 6 | mail: davy.vandeun@antwerpen.be 7 | organization: Stad Antwerpen 8 | project: MPA ANPR scanwagens 9 | comments: A project to acquire vehicles equipped with ANPR cameras for automated parking enforcement for the city of Antwerp. The project scope included the standardisation of information flow from vehicle to backend. 10 | startDate: 1/1/2022 11 | - 12 | adopter: Robert De Beukeulaer 13 | description: Domain Specialist 14 | mail: robert.debeukelaer@digipolis.be 15 | organization: Digipolis Antwerpen 16 | project: MPA ANPR scanwagens 17 | comments: Project to set up the necessary integration components to facilitate information flow from the ALPR scanning vehicle to the parking enforcement software. 18 | startDate: 1/10/2022 19 | - 20 | adopter: Martijn Oostdam 21 | description: Project Manager 22 | mail: m.oostdam@arvoo.com 23 | organization: ARVOO 24 | project: MPA ANPR Scanwagens 25 | comments: ANPR Scanning equipment for Scancars 26 | startDate: 1/10/2022 27 | 28 | -------------------------------------------------------------------------------- /APDSObservation/LICENSE.md: -------------------------------------------------------------------------------- 1 | Copyright (c) 2022-2023, APDS (Alliance for Parking Data Standards) 2 | 3 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: 4 | 5 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 6 | 7 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -------------------------------------------------------------------------------- /APDSObservation/code/README.md: -------------------------------------------------------------------------------- 1 | # APDSObservation 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_APDSObservation.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/APDSObservation/code/code_for_using_dataModel.Transportation_APDSObservation.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /APDSObservation/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | This model captures the dataset of an observation of a Vehicle on a specific time at a particular location. It is derived from the APDS (Alliance for Parking DataStandard) specification. The observation method includes the usage of ALPR cameras, visual observation, scanner, or any other meansof observation. 3 | notesMiddle: 4 | 5 | notesFooter: 6 | -------------------------------------------------------------------------------- /AnonymousCommuterId/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model AnonymousCommuterId of the Subject incubated/SMARTCITIES. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: DTI Huesca 5 | description: Data model for anonymous detection of people and anonymous flow monitoring 6 | mail: 7 | organization: Huesca council 8 | project: DTI Huesca 9 | comments: 10 | startDate: August 2021 11 | - 12 | adopter: Galdakao 13 | description: Data model for anonymous detection of people and anonymous flow monitoring 14 | mail: 15 | organization: Galdakao city hall 16 | project: Galdakao 17 | comments: 18 | startDate: July 2021 19 | -------------------------------------------------------------------------------- /AnonymousCommuterId/code/README.md: -------------------------------------------------------------------------------- 1 | # AnonymousCommuterId 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_AnonymousCommuterId.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/AnonymousCommuterId/code/code_for_using_dataModel.Transportation_AnonymousCommuterId.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /AnonymousCommuterId/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "ngsi-ld:HUES:001", 3 | "anonymizedId": { 4 | "type": "Text", 5 | "value": "D20220AC3478565F" 6 | }, 7 | "type": "AnonymousCommuterId", 8 | "orig": { 9 | "type": "Text", 10 | "value": "City hall" 11 | }, 12 | "dest": { 13 | "type": "Text", 14 | "value": "Library" 15 | }, 16 | "location": { 17 | "type": "geo:json", 18 | "value": { 19 | "type": "Point", 20 | "coordinates": [ 21 | 43.23161118206764, 22 | -2.844695196525928 23 | ] 24 | } 25 | }, 26 | "date": { 27 | "type": "DateTime", 28 | "value": "2022-09-05T08:25:35.00Z" 29 | }, 30 | "algorithm": { 31 | "type": "Text", 32 | "value": "SHA1" 33 | }, 34 | "dateCreated": { 35 | "type": "DateTime", 36 | "value": "2022-09-05T09:25:35.00Z" 37 | }, 38 | "dateModified": { 39 | "type": "DateTime", 40 | "value": "2022-09-12T09:25:35.00Z" 41 | } 42 | } -------------------------------------------------------------------------------- /AnonymousCommuterId/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "anonymizedId__type_", "anonymizedId__value_", "type_", "orig__type_", "orig__value_", "dest__type_", "dest__value_", "location__type_", "location__value__type_", "location__value__coordinates__0_", "location__value__coordinates__1_", "date__type_", "date__value_", "algorithm__type_", "algorithm__value_", "dateCreated__type_", "dateCreated__value_", "dateModified__type_", "dateModified__value_" 2 | "ngsi-ld:HUES:001", "Text", "D20220AC3478565F", "AnonymousCommuterId", "Text", "City hall", "Text", "Library", "geo:json", "Point", "43.23161118206764", "-2.844695196525928", "DateTime", "2022-09-05T08:25:35.00Z", "Text", "SHA1", "DateTime", "2022-09-05T09:25:35.00Z", "DateTime", "2022-09-12T09:25:35.00Z" -------------------------------------------------------------------------------- /AnonymousCommuterId/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "ngsi-ld:HUES:001", 3 | "anonymizedId": { 4 | "type": "Property", 5 | "value": "D20220AC3478565F" 6 | }, 7 | "type": "AnonymousCommuterId", 8 | "orig": { 9 | "type": "Property", 10 | "value": "City hall" 11 | }, 12 | "dest": { 13 | "type": "Property", 14 | "value": "Library" 15 | }, 16 | "location": { 17 | "type": "GeoProperty", 18 | "value": { 19 | "type": "Point", 20 | "coordinates": [ 21 | 43.23161118206764, 22 | -2.844695196525928 23 | ] 24 | } 25 | }, 26 | "date": { 27 | "type": "Property", 28 | "value": "2022-09-05T08:25:35.00Z" 29 | }, 30 | "algorithm": { 31 | "type": "Property", 32 | "value": "SHA1" 33 | }, 34 | "dateCreated": { 35 | "type": "Property", 36 | "value": "2022-09-05T09:25:35.00Z" 37 | }, 38 | "dateModified": { 39 | "type": "Property", 40 | "value": "2022-09-12T09:25:35.00Z" 41 | }, 42 | "@context": [ 43 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 44 | ] 45 | } 46 | -------------------------------------------------------------------------------- /AnonymousCommuterId/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "anonymizedId__type_", "anonymizedId__value_", "type_", "orig__type_", "orig__value_", "dest__type_", "dest__value_", "location__type_", "location__value__type_", "location__value__coordinates__0_", "location__value__coordinates__1_", "date__type_", "date__value_", "algorithm__type_", "algorithm__value_", "dateCreated__type_", "dateCreated__value_", "dateModified__type_", "dateModified__value_", "@context__0_" 2 | "ngsi-ld:HUES:001", "Property", "D20220AC3478565F", "AnonymousCommuterId", "Property", "City hall", "Property", "Library", "GeoProperty", "Point", "43.23161118206764", "-2.844695196525928", "Property", "2022-09-05T08:25:35.00Z", "Property", "SHA1", "Property", "2022-09-05T09:25:35.00Z", "Property", "2022-09-12T09:25:35.00Z", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /AnonymousCommuterId/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "ngsi-ld:HUES:001", 3 | "anonymizedId": "D20220AC3478565F", 4 | "type": "AnonymousCommuterId", 5 | "date": "2022-09-05T08:25:35.00Z", 6 | "orig": "City hall", 7 | "dest": "Library", 8 | "source": "People Monitoring", 9 | "algorithm": "SHA1", 10 | "dateCreated": "2022-09-05T09:25:35.00Z", 11 | "dateModified": "2022-09-12T09:25:35.00Z", 12 | "location": { 13 | "type": "Point", 14 | "coordinates": [ 15 | 43.23161118206764, 16 | -2.844695196525928 17 | ] 18 | } 19 | } -------------------------------------------------------------------------------- /AnonymousCommuterId/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "anonymizedId_", "type_", "date_", "orig_", "dest_", "source_", "algorithm_", "dateCreated_", "dateModified_", "location__type_", "location__coordinates__0_", "location__coordinates__1_" 2 | "ngsi-ld:HUES:001", "D20220AC3478565F", "AnonymousCommuterId", "2022-09-05T08:25:35.00Z", "City hall", "Library", "People Monitoring", "SHA1", "2022-09-05T09:25:35.00Z", "2022-09-12T09:25:35.00Z", "Point", "43.23161118206764", "-2.844695196525928" -------------------------------------------------------------------------------- /AnonymousCommuterId/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "ngsi-ld:HUES:001", 3 | "anonymizedId": "D20220AC3478565F", 4 | "type": "AnonymousCommuterId", 5 | "date": "2022-09-05T08:25:35.00Z", 6 | "orig": "City hall", 7 | "dest": "Library", 8 | "source": "People Monitoring", 9 | "algorithm": "SHA1", 10 | "dateCreated": "2022-09-05T09:25:35.00Z", 11 | "dateModified": "2022-09-12T09:25:35.00Z", 12 | "location": { 13 | "type": "Point", 14 | "coordinates": [ 15 | 43.23161118206764, 16 | -2.844695196525928 17 | ] 18 | }, 19 | "@context": [ 20 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 21 | ] 22 | } -------------------------------------------------------------------------------- /AnonymousCommuterId/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "anonymizedId_", "type_", "date_", "orig_", "dest_", "source_", "algorithm_", "dateCreated_", "dateModified_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "@context__0_" 2 | "ngsi-ld:HUES:001", "D20220AC3478565F", "AnonymousCommuterId", "2022-09-05T08:25:35.00Z", "City hall", "Library", "People Monitoring", "SHA1", "2022-09-05T09:25:35.00Z", "2022-09-12T09:25:35.00Z", "Point", "43.23161118206764", "-2.844695196525928", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /AnonymousCommuterId/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | This is model is intended to be used when some PII tracking is needed, and thus it's needed to anonymize the identifiers in order to still provide some useful insights, but using a non-reversible anonymizing (hashing) function. As it is usually the case, there are provisioned standardized attributes to indicate the current and previous location of the detection. They are intended to hold another entity ID, because having the detectors also replicated in the form of entities provides a much better data modelling experience. Finally, an algorithm attribute was added in order to aid with the several ways and methodologies of anonymizing PII. 3 | 4 | notesMiddle: 5 | 6 | notesFooter: 7 | This model was contributed by Purple Blob S.L., and tailored according to the views and necessities of our METIS anonymized people flow product. We are open about the development of a wide use AnonymousCommuterId interoperable data model, and thus, feel free to contact adelgado@purpleblob.net or iruiz@purpleblob.net for additional discussion, or even better, open a Github Issue! 8 | 9 | notesReadme: 10 | 11 | -------------------------------------------------------------------------------- /AnonymousCommuterId/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model AnonymousCommuterId of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE AnonymousCommuterId_type AS ENUM ('AnonymousCommuterId'); 3 | CREATE TABLE AnonymousCommuterId (address JSON, algorithm TEXT, alternateName TEXT, anonymizedId TEXT, areaServed TEXT, dataProvider TEXT, date TIMESTAMP, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, dest TEXT, id TEXT PRIMARY KEY, location JSON, name TEXT, orig TEXT, owner JSON, seeAlso JSON, source TEXT, type AnonymousCommuterId_type); -------------------------------------------------------------------------------- /AnprFlowObserved/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model [Data model] of the Subject [Subject]. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: Ruud Reunis 5 | description: Describes the registration of vehicles by an ANPR camera 6 | mail: ruud.reunis@inuits.eu 7 | organization: Inuits 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /AnprFlowObserved/code/README.md: -------------------------------------------------------------------------------- 1 | # AnprFlowObserved 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_AnprFlowObserved.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/AnprFlowObserved/code/code_for_using_dataModel.Transportation_AnprFlowObserved.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /AnprFlowObserved/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "address__type_", "address__value__addressCountry_", "address__value__addressLocality_", "address__value__streetAddress_", "dateObserved__type_", "dateObserved__value_", "laneId__type_", "laneId__value_", "areaServed__type_", "areaServed__value_", "zonesServed__type_", "zonesServed__value__0_", "vehiclePlateNotRead__type_", "vehiclePlateNotRead__value_", "observedVehicle__type_", "observedVehicle__value__direction_", "observedVehicle__value__speed_", "observedVehicle__value__brand__name_", "observedVehicle__value__brand__confidence_", "observedVehicle__value__model__name_", "observedVehicle__value__model__confidence_", "observedVehicle__value__color__name_", "observedVehicle__value__color__confidence_", "observedVehicle__value__country__code_", "observedVehicle__value__country__confidence_", "observedVehicle__value__licensePlate__identifier_", "observedVehicle__value__licensePlate__confidence_", "refImages__type_", "refImages__value__0__url_", "refImages__value__0__contentType_", "refImages__value__0__imageType_", "location__type_", "location__value__coordinates__0_", "location__value__coordinates__1_", "location__value__type_" 2 | "anprFlowObserved:LEZ-Noorderlaan", "AnprFlowObserved", "StructuredValue", "BE", "Antwerp", "Noorderlaan", "DateTime", "2022-09-01T16:30:00Z", "Text", "ABC123", "Text", "Antwerp", "StructuredValue", "Antwerp", "Boolean", "False", "StructuredValue", "towards", "50", "Audi", "0.97", "A3", "0.98", "black", "0.95", "BE", "0.95", "1-ABC-123", "0.96", "StructuredValue", "s3://bucket/object-xxx-plate", "image/jpg", "anpr", "geo:json", "-56.6404505", "168.370658", "Point" -------------------------------------------------------------------------------- /AnprFlowObserved/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "anprFlowObserved:LEZ-Noorderlaan", 3 | "type": "AnprFlowObserved", 4 | "address": { 5 | "type": "Property", 6 | "value": { 7 | "addressCountry": "BE", 8 | "addressLocality": "Antwerp", 9 | "streetAddress": "Noorderlaan" 10 | } 11 | }, 12 | "dateObserved": { 13 | "type": "Property", 14 | "value": { 15 | "@type": "DateTime", 16 | "@value": "2022-09-01T16:30:00Z" 17 | } 18 | }, 19 | "laneId": { 20 | "type": "Property", 21 | "value": "ABC123" 22 | }, 23 | "areaServed": { 24 | "type": "Property", 25 | "value": "Antwerp" 26 | }, 27 | "zonesServed": { 28 | "type": "Property", 29 | "value": { 30 | "type": "string", 31 | "coordinates": [ 32 | "Antwerp" 33 | ] 34 | } 35 | }, 36 | "vehiclePlateNotRead": { 37 | "type": "Property", 38 | "value": false 39 | }, 40 | "observedVehicle": { 41 | "type": "Property", 42 | "value": { 43 | "direction": "towards", 44 | "speed": 50, 45 | "brand": "Audi", 46 | "model": "A3", 47 | "color": "black", 48 | "country": "BE", 49 | "licensePlate": "1-ABC-123" 50 | } 51 | }, 52 | "refImages": { 53 | "type": "Property", 54 | "value": [ 55 | { 56 | "type": "s3://bucket/object-xxx-plate", 57 | "contentType": "image/jpg", 58 | "imageType": "anpr" 59 | } 60 | ] 61 | }, 62 | "@context": [ 63 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 64 | ] 65 | } 66 | -------------------------------------------------------------------------------- /AnprFlowObserved/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "address__type_", "address__value__addressCountry_", "address__value__addressLocality_", "address__value__streetAddress_", "dateObserved__type_", "dateObserved__value__@type_", "dateObserved__value__@value_", "laneId__type_", "laneId__value_", "areaServed__type_", "areaServed__value_", "zonesServed__type_", "zonesServed__value__type_", "zonesServed__value__coordinates__0_", "vehiclePlateNotRead__type_", "vehiclePlateNotRead__value_", "observedVehicle__type_", "observedVehicle__value__direction_", "observedVehicle__value__speed_", "observedVehicle__value__brand_", "observedVehicle__value__model_", "observedVehicle__value__color_", "observedVehicle__value__country_", "observedVehicle__value__licensePlate_", "refImages__type_", "refImages__value__0__type_", "refImages__value__0__contentType_", "refImages__value__0__imageType_", "@context__0_" 2 | "anprFlowObserved:LEZ-Noorderlaan", "AnprFlowObserved", "Property", "BE", "Antwerp", "Noorderlaan", "Property", "DateTime", "2022-09-01T16:30:00Z", "Property", "ABC123", "Property", "Antwerp", "Property", "string", "Antwerp", "Property", "False", "Property", "towards", "50", "Audi", "A3", "black", "BE", "1-ABC-123", "Property", "s3://bucket/object-xxx-plate", "image/jpg", "anpr", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /AnprFlowObserved/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "anprFlowObserved:LEZ-Noorderlaan", 3 | "type": "AnprFlowObserved", 4 | "address": { 5 | "addressCountry": "BE", 6 | "addressLocality": "Antwerp", 7 | "streetAddress": "Noorderlaan" 8 | }, 9 | "dateObserved": "2022-09-01T16:30:00Z", 10 | "dateReceived": "2022-09-01T16:35:00Z", 11 | "observedBy": "ANPR1_Noorderlaan", 12 | "laneId": "ABC123", 13 | "areaServed": "Antwerp", 14 | "zonesServed": [ 15 | "Antwerp" 16 | ], 17 | "vehiclePlateNotRead": false, 18 | "observedVehicle": { 19 | "direction": "towards", 20 | "speed": 50, 21 | "brand": { 22 | "name": "Audi", 23 | "confidence": 0.97 24 | }, 25 | "model": { 26 | "name": "A3", 27 | "confidence": 0.98 28 | }, 29 | "color": { 30 | "name": "black", 31 | "confidence": 0.95 32 | }, 33 | "country": { 34 | "code": "BE", 35 | "confidence": 0.95 36 | }, 37 | "licensePlate": { 38 | "identifier": "1-ABC-123", 39 | "confidence": 0.96 40 | } 41 | }, 42 | "location": { 43 | "type": "Point", 44 | "coordinates": [ 45 | -56.6404505, 46 | 168.370658 47 | ] 48 | }, 49 | "refImages": [ 50 | { 51 | "contentType": "image/jpg", 52 | "imageType": "anpr", 53 | "url": "urn:ngsi-ld:ANPR:items:123" 54 | } 55 | ] 56 | } -------------------------------------------------------------------------------- /AnprFlowObserved/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "address__addressCountry_", "address__addressLocality_", "address__streetAddress_", "dateObserved_", "dateReceived_", "observedBy_", "laneId_", "areaServed_", "zonesServed__0_", "vehiclePlateNotRead_", "observedVehicle__direction_", "observedVehicle__speed_", "observedVehicle__brand__name_", "observedVehicle__brand__confidence_", "observedVehicle__model__name_", "observedVehicle__model__confidence_", "observedVehicle__color__name_", "observedVehicle__color__confidence_", "observedVehicle__country__code_", "observedVehicle__country__confidence_", "observedVehicle__licensePlate__identifier_", "observedVehicle__licensePlate__confidence_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "refImages__0__contentType_", "refImages__0__imageType_", "refImages__0__url_" 2 | "anprFlowObserved:LEZ-Noorderlaan", "AnprFlowObserved", "BE", "Antwerp", "Noorderlaan", "2022-09-01T16:30:00Z", "2022-09-01T16:35:00Z", "ANPR1_Noorderlaan", "ABC123", "Antwerp", "Antwerp", "False", "towards", "50", "Audi", "0.97", "A3", "0.98", "black", "0.95", "BE", "0.95", "1-ABC-123", "0.96", "Point", "-56.6404505", "168.370658", "image/jpg", "anpr", "urn:ngsi-ld:ANPR:items:123" -------------------------------------------------------------------------------- /AnprFlowObserved/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "anprFlowObserved:LEZ-Noorderlaan", 3 | "type": "AnprFlowObserved", 4 | "address": { 5 | "addressCountry": "BE", 6 | "addressLocality": "Antwerp", 7 | "streetAddress": "Noorderlaan" 8 | }, 9 | "dateObserved": "2022-09-01T16:30:00Z", 10 | "dateReceived": "2022-09-01T16:35:00Z", 11 | "observedBy": "ANPR1_Noorderlaan", 12 | "laneId": "ABC123", 13 | "areaServed": "Antwerp", 14 | "zonesServed": [ 15 | "Antwerp" 16 | ], 17 | "vehiclePlateNotRead": false, 18 | "observedVehicle": { 19 | "direction": "towards", 20 | "speed": 50, 21 | "brand": { 22 | "name": "Audi", 23 | "confidence": 0.97 24 | }, 25 | "model": { 26 | "name": "A3", 27 | "confidence": 0.98 28 | }, 29 | "color": { 30 | "name": "black", 31 | "confidence": 0.95 32 | }, 33 | "country": { 34 | "code": "BE", 35 | "confidence": 0.95 36 | }, 37 | "licensePlate": { 38 | "identifier": "1-ABC-123", 39 | "confidence": 0.96 40 | } 41 | }, 42 | "location": { 43 | "type": "Point", 44 | "coordinates": [ 45 | -56.6404505, 46 | 168.370658 47 | ] 48 | }, 49 | "refImages": [ 50 | { 51 | "contentType": "image/jpg", 52 | "imageType": "anpr", 53 | "url": "urn:ngsi-ld:ANPR:items:123" 54 | } 55 | ], 56 | "@context": [ 57 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 58 | ] 59 | } -------------------------------------------------------------------------------- /AnprFlowObserved/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "address__addressCountry_", "address__addressLocality_", "address__streetAddress_", "dateObserved_", "dateReceived_", "observedBy_", "laneId_", "areaServed_", "zonesServed__0_", "vehiclePlateNotRead_", "observedVehicle__direction_", "observedVehicle__speed_", "observedVehicle__brand__name_", "observedVehicle__brand__confidence_", "observedVehicle__model__name_", "observedVehicle__model__confidence_", "observedVehicle__color__name_", "observedVehicle__color__confidence_", "observedVehicle__country__code_", "observedVehicle__country__confidence_", "observedVehicle__licensePlate__identifier_", "observedVehicle__licensePlate__confidence_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "refImages__0__contentType_", "refImages__0__imageType_", "refImages__0__url_", "@context__0_" 2 | "anprFlowObserved:LEZ-Noorderlaan", "AnprFlowObserved", "BE", "Antwerp", "Noorderlaan", "2022-09-01T16:30:00Z", "2022-09-01T16:35:00Z", "ANPR1_Noorderlaan", "ABC123", "Antwerp", "Antwerp", "False", "towards", "50", "Audi", "0.97", "A3", "0.98", "black", "0.95", "BE", "0.95", "1-ABC-123", "0.96", "Point", "-56.6404505", "168.370658", "image/jpg", "anpr", "urn:ngsi-ld:ANPR:items:123", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /AnprFlowObserved/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: This data model describes the main entities involved with smart applications that deal with police issues. This set of entities is primarily associated with the Automotive and Smart City vertical segments and related IoT applications. When feasible, references to existing schema.org entity types and attributes are included. This model has been devised to be as generic as possible, thus allowing to be used by different police departements and zones like ANPR, Trajectory control and Police Vehicles 2 | 3 | notesMiddle: 4 | 5 | notesFooter: 6 | -------------------------------------------------------------------------------- /AnprFlowObserved/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model AnprFlowObserved of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE AnprFlowObserved_type AS ENUM ('AnprFlowObserved'); 3 | CREATE TABLE AnprFlowObserved (address JSON, alternateName TEXT, areaServed TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, dateObserved TIMESTAMP, dateReceived TIMESTAMP, description TEXT, id TEXT PRIMARY KEY, laneId TEXT, location JSON, name TEXT, observedVehicle JSON, owner JSON, refImages JSON, seeAlso JSON, source TEXT, type AnprFlowObserved_type, vehiclePlateNotRead BOOLEAN, zonesServed JSON); -------------------------------------------------------------------------------- /BikeHireDockingStation/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model BikeHireDockingStation of the Subject Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: IUDX 5 | description: A citizen bike hiring system Data Model. 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /BikeHireDockingStation/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/BikeHireDockingStation/SmartDataModelBadge.png -------------------------------------------------------------------------------- /BikeHireDockingStation/code/README.md: -------------------------------------------------------------------------------- 1 | # BikeHireDockingStation 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_BikeHireDockingStation.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/BikeHireDockingStation/code/code_for_using_dataModel.Transportation_BikeHireDockingStation.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /BikeHireDockingStation/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "status__type_", "status__value_", "provider__type_", "provider__value_", "contactPoint__type_", "contactPoint__value__url_", "availableBikeNumber__type_", "availableBikeNumber__value_", "freeSlotNumber__type_", "freeSlotNumber__value_", "openingHours__type_", "openingHours__value_", "location__type_", "location__value__type_", "location__value__coordinates__0_", "location__value__coordinates__1_", "address__type_", "address__value__addressCountry_", "address__value__addressLocality_", "address__value__streetAddress_", "totalSlotNumber__type_", "totalSlotNumber__value_", "outOfServiceSlotNumber__type_", "outOfServiceSlotNumber__value_", "stationName__type_", "stationName__value_", "mediaURL__type_", "mediaURL__value_", "agency_url__type_", "agency_url__value_", "agency_name__type_", "agency_name__value_", "stationCode__type_", "stationCode__value_", "observationDate__type_", "observationDate__value_", "agency_fare_url__type_", "agency_fare_url__value_" 2 | "urn:ngsi-ld:Bcn-BikeHireDockingStation-1", "BikeHireDockingStation", "Text", "working", "Text", "University of Mumbay", "StructuredValue", "uri:ngsi:www.lignesdazur.com", "Number", "20", "Number", "10", "Text", "Mo-Fr 10:00-19:00, Sa 10:00-22:00, Su 10:00-21:00", "geo:json", "Point", "2.180042", "41.397952", "StructuredValue", "ES", "Barcelona", "Gran Via Corts Catalanes,760", "Number", "100", "Number", "21", "Text", "Pune", "Text", "http://pedalsaddle.in/", "Text", "http://pedalsaddle.in/", "Text", "PedalSaddle", "Text", "2", "DateTime", "2021-03-11T15:51:02+05:30", "Text", "" -------------------------------------------------------------------------------- /BikeHireDockingStation/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:Bcn-BikeHireDockingStation-1", 3 | "type": "BikeHireDockingStation", 4 | "status": "working", 5 | "provider": "University of Mumbay", 6 | "contactPoint": { 7 | "url": "uri:ngsi:www.lignesdazur.com" 8 | }, 9 | "availableBikeNumber": 20, 10 | "freeSlotNumber": 10, 11 | "openingHours": "Mo-Fr 10:00-19:00, Sa 10:00-22:00, Su 10:00-21:00", 12 | "location": { 13 | "type": "Point", 14 | "coordinates": [ 15 | 2.180042, 16 | 41.397952 17 | ] 18 | }, 19 | "address": { 20 | "addressCountry": "ES", 21 | "addressLocality": "Barcelona", 22 | "streetAddress": "Gran Via Corts Catalanes,760" 23 | }, 24 | "totalSlotNumber": 100, 25 | "outOfServiceSlotNumber": 21, 26 | "stationName": "Pune", 27 | "mediaURL": "http://pedalsaddle.in/", 28 | "agency_url": "http://pedalsaddle.in/", 29 | "agency_name": "PedalSaddle", 30 | "stationCode": "2", 31 | "observationDate": "2021-03-11T15:51:02+05:30", 32 | "agency_fare_url": "" 33 | } -------------------------------------------------------------------------------- /BikeHireDockingStation/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "status_", "provider_", "contactPoint__url_", "availableBikeNumber_", "freeSlotNumber_", "openingHours_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "address__addressCountry_", "address__addressLocality_", "address__streetAddress_", "totalSlotNumber_", "outOfServiceSlotNumber_", "stationName_", "mediaURL_", "agency_url_", "agency_name_", "stationCode_", "observationDate_", "agency_fare_url_" 2 | "urn:ngsi-ld:Bcn-BikeHireDockingStation-1", "BikeHireDockingStation", "working", "University of Mumbay", "uri:ngsi:www.lignesdazur.com", "20", "10", "Mo-Fr 10:00-19:00, Sa 10:00-22:00, Su 10:00-21:00", "Point", "2.180042", "41.397952", "ES", "Barcelona", "Gran Via Corts Catalanes,760", "100", "21", "Pune", "http://pedalsaddle.in/", "http://pedalsaddle.in/", "PedalSaddle", "2", "2021-03-11T15:51:02+05:30", "" -------------------------------------------------------------------------------- /BikeHireDockingStation/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:Bcn-BikeHireDockingStation-1", 3 | "type": "BikeHireDockingStation", 4 | "address": { 5 | "addressCountry": "ES", 6 | "addressLocality": "Barcelona", 7 | "streetAddress": "Gran Via Corts Catalanes,760" 8 | }, 9 | "agency_fare_url": "", 10 | "agency_name": "PedalSaddle", 11 | "agency_url": "http://pedalsaddle.in/", 12 | "availableBikeNumber": 20, 13 | "contactPoint": { 14 | "url": "uri:ngsi:www.lignesdazur.com" 15 | }, 16 | "freeSlotNumber": 10, 17 | "location": { 18 | "type": "Point", 19 | "coordinates": [ 20 | 2.180042, 21 | 41.397952 22 | ] 23 | }, 24 | "mediaURL": "http://pedalsaddle.in/", 25 | "observationDate": "2021-03-11T15:51:02+05:30", 26 | "openingHours": "Mo-Fr 10:00-19:00, Sa 10:00-22:00, Su 10:00-21:00", 27 | "outOfServiceSlotNumber": 21, 28 | "provider": "University of Mumbay", 29 | "stationCode": "2", 30 | "stationName": "Pune", 31 | "status": "working", 32 | "totalSlotNumber": 100, 33 | "@context": [ 34 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 35 | ] 36 | } -------------------------------------------------------------------------------- /BikeHireDockingStation/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "address__addressCountry_", "address__addressLocality_", "address__streetAddress_", "agency_fare_url_", "agency_name_", "agency_url_", "availableBikeNumber_", "contactPoint__url_", "freeSlotNumber_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "mediaURL_", "observationDate_", "openingHours_", "outOfServiceSlotNumber_", "provider_", "stationCode_", "stationName_", "status_", "totalSlotNumber_", "@context__0_" 2 | "urn:ngsi-ld:Bcn-BikeHireDockingStation-1", "BikeHireDockingStation", "ES", "Barcelona", "Gran Via Corts Catalanes,760", "", "PedalSaddle", "http://pedalsaddle.in/", "20", "uri:ngsi:www.lignesdazur.com", "10", "Point", "2.180042", "41.397952", "http://pedalsaddle.in/", "2021-03-11T15:51:02+05:30", "Mo-Fr 10:00-19:00, Sa 10:00-22:00, Su 10:00-21:00", "21", "University of Mumbay", "2", "Pune", "working", "100", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /BikeHireDockingStation/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | Many cities provide a bike hiring system for citizens. These can hire a bike base on different types of subscriptions. A bike hire docking station where subscribed users can hire and return a bike. It provides data about its main features and availability of bikes and free slots. 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /BikeHireDockingStation/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model BikeHireDockingStation of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE status_type AS ENUM ('almostEmpty','almostFull','empty','full','outOfService','withIncidence','working');CREATE TYPE BikeHireDockingStation_type AS ENUM ('BikeHireDockingStation'); 3 | CREATE TABLE BikeHireDockingStation (address JSON, agency_fare_url TEXT, agency_name TEXT, agency_url TEXT, alternateName TEXT, areaServed TEXT, availableBikeNumber NUMERIC, contactPoint JSON, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, freeSlotNumber NUMERIC, id TEXT PRIMARY KEY, location JSON, mediaURL TEXT, name TEXT, observationDateTime TIMESTAMP, openingHours TEXT, outOfServiceSlotNumber NUMERIC, owner JSON, provider TEXT, seeAlso JSON, source TEXT, stationCode TEXT, stationName TEXT, status status_type, totalSlotNumber NUMERIC, type BikeHireDockingStation_type); -------------------------------------------------------------------------------- /BikeHireDockingStation/swagger.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | # Copyleft (c) 2022 Contributors to Smart Data Models initiative 3 | # 4 | 5 | 6 | components: 7 | schemas: 8 | BikeHireDockingStation: 9 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/BikeHireDockingStation/model.yaml#/BikeHireDockingStation" 10 | info: 11 | description: | 12 | Bike Hire Docking Station 13 | title: BikeHireDockingStation 14 | version: "0.1.1" 15 | openapi: "3.0.0" 16 | 17 | paths: 18 | /ngsi-ld/v1/entities: 19 | get: 20 | description: "Retrieve a set of entities which matches a specific query from an NGSI-LD system" 21 | parameters: 22 | - 23 | in: query 24 | name: type 25 | required: true 26 | schema: 27 | enum: 28 | - BikeHireDockingStation 29 | type: string 30 | responses: 31 | ? "200" 32 | : 33 | content: 34 | application/ld+json: 35 | examples: 36 | keyvalues: 37 | summary: "Key-Values Pairs" 38 | value: 39 | - 40 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/BikeHireDockingStation/examples/example.json" 41 | normalized: 42 | summary: "Normalized NGSI-LD" 43 | value: 44 | - 45 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/BikeHireDockingStation/examples/example-normalized.jsonld" 46 | description: OK 47 | tags: 48 | - ngsi-ld 49 | tags: 50 | - 51 | description: "NGSI-LD Linked-data Format" 52 | name: ngsi-ld 53 | -------------------------------------------------------------------------------- /BikeLane/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model BikeLane of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /BikeLane/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/BikeLane/SmartDataModelBadge.png -------------------------------------------------------------------------------- /BikeLane/code/README.md: -------------------------------------------------------------------------------- 1 | # BikeLane 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_BikeLane.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/BikeLane/code/code_for_using_dataModel.Transportation_BikeLane.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /BikeLane/examples/example-geojsonfeature.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:BikeLane:BikeLane-AveMed-Benidorm-123456", 3 | "type": "Feature", 4 | "geometry": { 5 | "coordinates": [ 6 | -8.768460000000001, 7 | 42.60214472222222 8 | ], 9 | "type": "Point" 10 | }, 11 | "properties": { 12 | "@context": [ 13 | "https://smartdatamodels.org/context.jsonld", 14 | "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld" 15 | ], 16 | "type": "BikeLane", 17 | "dateObserved": "2021-02-20T06:45:00Z", 18 | "location": { 19 | "coordinates": [ 20 | -8.768460000000001, 21 | 42.60214472222222 22 | ], 23 | "type": "Point" 24 | }, 25 | "name": "Carril bici - Avenida del Mediterr\u00e1neo", 26 | "description": "Informaci\u00f3n del carril bici", 27 | "address": { 28 | "streetAddress": "37 Avenida del Mediterr\u00e1neo", 29 | "addressCountry": "ES", 30 | "addressLocality": "Benidorm" 31 | }, 32 | "laneOccupancy": 7, 33 | "laneWidth": 2, 34 | "laneLength": 150, 35 | "dataProvider": "LaneSensor-12345" 36 | } 37 | } -------------------------------------------------------------------------------- /BikeLane/examples/example-geojsonfeature.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "geometry__coordinates__0_", "geometry__coordinates__1_", "geometry__type_", "properties__@context__0_", "properties__@context__1_", "properties__type_", "properties__dateObserved_", "properties__location__coordinates__0_", "properties__location__coordinates__1_", "properties__location__type_", "properties__name_", "properties__description_", "properties__address__streetAddress_", "properties__address__addressCountry_", "properties__address__addressLocality_", "properties__laneOccupancy_", "properties__laneWidth_", "properties__laneLength_", "properties__dataProvider_" 2 | "urn:ngsi-ld:BikeLane:BikeLane-AveMed-Benidorm-123456", "Feature", "-8.768460000000001", "42.60214472222222", "Point", "https://smartdatamodels.org/context.jsonld", "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld", "BikeLane", "2021-02-20T06:45:00Z", "-8.768460000000001", "42.60214472222222", "Point", "Carril bici - Avenida del Mediterráneo", "Información del carril bici", "37 Avenida del Mediterráneo", "ES", "Benidorm", "7", "2", "150", "LaneSensor-12345" -------------------------------------------------------------------------------- /BikeLane/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "BikeLane-AveMed-Benidorm-123456", 3 | "type": "BikeLane", 4 | "dateObserved": { 5 | "type": "DateTime", 6 | "value": "2021-02-20T06:45:00Z" 7 | }, 8 | "location": { 9 | "type": "geo:json", 10 | "value": { 11 | "type": "Point", 12 | "coordinates": [ 13 | -8.768460000000001, 14 | 42.60214472222222 15 | ] 16 | } 17 | }, 18 | "name": { 19 | "type": "Text", 20 | "value": "Carril bici - Avenida del Mediterr\u00e1neo" 21 | }, 22 | "description": { 23 | "type": "Text", 24 | "value": "Informaci\u00f3n del carril bici" 25 | }, 26 | "address": { 27 | "type": "StructuredValue", 28 | "value": { 29 | "streetAddress": "37 Avenida del Mediterr\u00e1neo", 30 | "addressCountry": "ES", 31 | "addressLocality": "Benidorm" 32 | } 33 | }, 34 | "dataProvider": { 35 | "type": "Text", 36 | "value": "LaneSensor-12345" 37 | }, 38 | "laneOccupancy": { 39 | "type": "Number", 40 | "value": 7 41 | }, 42 | "laneWidth": { 43 | "type": "Number", 44 | "value": 2 45 | }, 46 | "laneLength": { 47 | "type": "Number", 48 | "value": 150 49 | } 50 | } -------------------------------------------------------------------------------- /BikeLane/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dateObserved__type_", "dateObserved__value_", "location__type_", "location__value__type_", "location__value__coordinates__0_", "location__value__coordinates__1_", "name__type_", "name__value_", "description__type_", "description__value_", "address__type_", "address__value__streetAddress_", "address__value__addressCountry_", "address__value__addressLocality_", "dataProvider__type_", "dataProvider__value_", "laneOccupancy__type_", "laneOccupancy__value_", "laneWidth__type_", "laneWidth__value_", "laneLength__type_", "laneLength__value_" 2 | "BikeLane-AveMed-Benidorm-123456", "BikeLane", "DateTime", "2021-02-20T06:45:00Z", "geo:json", "Point", "-8.768460000000001", "42.60214472222222", "Text", "Carril bici - Avenida del Mediterráneo", "Text", "Información del carril bici", "StructuredValue", "37 Avenida del Mediterráneo", "ES", "Benidorm", "Text", "LaneSensor-12345", "Number", "7", "Number", "2", "Number", "150" -------------------------------------------------------------------------------- /BikeLane/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "BikeLane-AveMed-Benidorm-123456", 3 | "type": "BikeLane", 4 | "dateObserved": "2021-02-20T06:45:00Z", 5 | "location": { 6 | "type": "Point", 7 | "coordinates": [ 8 | -8.768460000000001, 9 | 42.60214472222222 10 | ] 11 | }, 12 | "name": "Carril bici - Avenida del Mediterr\u00e1neo", 13 | "description": "Informaci\u00f3n del carril bici", 14 | "address": { 15 | "streetAddress": "37 Avenida del Mediterr\u00e1neo", 16 | "addressCountry": "ES", 17 | "addressLocality": "Benidorm" 18 | }, 19 | "dataProvider": "LaneSensor-12345", 20 | "laneOccupancy": 7, 21 | "laneWidth": 2, 22 | "laneLength": 150 23 | } -------------------------------------------------------------------------------- /BikeLane/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dateObserved_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "name_", "description_", "address__streetAddress_", "address__addressCountry_", "address__addressLocality_", "dataProvider_", "laneOccupancy_", "laneWidth_", "laneLength_" 2 | "BikeLane-AveMed-Benidorm-123456", "BikeLane", "2021-02-20T06:45:00Z", "Point", "-8.768460000000001", "42.60214472222222", "Carril bici - Avenida del Mediterráneo", "Información del carril bici", "37 Avenida del Mediterráneo", "ES", "Benidorm", "LaneSensor-12345", "7", "2", "150" -------------------------------------------------------------------------------- /BikeLane/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:BikeLane:BikeLane-AveMed-Benidorm-123456", 3 | "type": "BikeLane", 4 | "address": { 5 | "streetAddress": "37 Avenida del Mediterr\u00e1neo", 6 | "addressCountry": "ES", 7 | "addressLocality": "Benidorm" 8 | }, 9 | "dataProvider": "LaneSensor-12345", 10 | "dateObserved": "2021-02-20T06:45:00Z", 11 | "description": "Informaci\u00f3n del carril bici", 12 | "laneLength": 150, 13 | "laneOccupancy": 7, 14 | "laneWidth": 2, 15 | "location": { 16 | "coordinates": [ 17 | -8.768460000000001, 18 | 42.60214472222222 19 | ], 20 | "type": "Point" 21 | }, 22 | "name": "Carril bici - Avenida del Mediterr\u00e1neo", 23 | "@context": [ 24 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 25 | ] 26 | } -------------------------------------------------------------------------------- /BikeLane/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "address__streetAddress_", "address__addressCountry_", "address__addressLocality_", "dataProvider_", "dateObserved_", "description_", "laneLength_", "laneOccupancy_", "laneWidth_", "location__coordinates__0_", "location__coordinates__1_", "location__type_", "name_", "@context__0_" 2 | "urn:ngsi-ld:BikeLane:BikeLane-AveMed-Benidorm-123456", "BikeLane", "37 Avenida del Mediterráneo", "ES", "Benidorm", "LaneSensor-12345", "2021-02-20T06:45:00Z", "Información del carril bici", "150", "7", "2", "-8.768460000000001", "42.60214472222222", "Point", "Carril bici - Avenida del Mediterráneo", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /BikeLane/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /BikeLane/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model BikeLane of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE BikeLane_type AS ENUM ('BikeLane'); 3 | CREATE TABLE BikeLane (address JSON, alternateName TEXT, areaServed TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, dateObserved TIMESTAMP, description TEXT, id TEXT PRIMARY KEY, laneLength NUMERIC, laneOccupancy NUMERIC, laneWidth NUMERIC, location JSON, name TEXT, owner JSON, seeAlso JSON, source TEXT, type BikeLane_type); -------------------------------------------------------------------------------- /BikeLane/swagger.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | # Copyleft (c) 2022 Contributors to Smart Data Models initiative 3 | # 4 | 5 | 6 | components: 7 | schemas: 8 | BikeLane: 9 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/BikeLane/model.yaml#/BikeLane" 10 | info: 11 | description: | 12 | A generic bike lane schema 13 | title: BikeLane 14 | version: "0.0.1" 15 | openapi: "3.0.0" 16 | 17 | paths: 18 | /ngsi-ld/v1/entities: 19 | get: 20 | description: "Retrieve a set of entities which matches a specific query from an NGSI-LD system" 21 | parameters: 22 | - 23 | in: query 24 | name: type 25 | required: true 26 | schema: 27 | enum: 28 | - BikeLane 29 | type: string 30 | responses: 31 | ? "200" 32 | : 33 | content: 34 | application/ld+json: 35 | examples: 36 | keyvalues: 37 | summary: "Key-Values Pairs" 38 | value: 39 | - 40 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/BikeLane/examples/example.json" 41 | normalized: 42 | summary: "Normalized NGSI-LD" 43 | value: 44 | - 45 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/BikeLane/examples/example-normalized.jsonld" 46 | description: OK 47 | tags: 48 | - ngsi-ld 49 | tags: 50 | - 51 | description: "NGSI-LD Linked-data Format" 52 | name: ngsi-ld 53 | -------------------------------------------------------------------------------- /CityWork/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model CityWork of the Subject Transportation. 2 | currentAdopters: 3 | - 4 | adopter: Stephane ROUX 5 | description: Project Manager 6 | mail: stephane.Roux@nicecotedazur.org 7 | organization: Métropole Nice Côte d'Azur 8 | project: Data Lake 9 | comments: SmartCity Project 10 | startDate: January 2019 11 | -------------------------------------------------------------------------------- /CityWork/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/CityWork/SmartDataModelBadge.png -------------------------------------------------------------------------------- /CityWork/code/README.md: -------------------------------------------------------------------------------- 1 | # CityWork 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_CityWork.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/CityWork/code/code_for_using_dataModel.Transportation_CityWork.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /CityWork/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /CrowdFlowObserved/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model CrowdFlowObserved of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: Las Rozas Innova 5 | description: 6 | mail: jpena@lasrozasinnova.es 7 | organization: Municipality of Las Rozas (Spain) 8 | project: https://lasrozasinnova.es/cosmos-plataforma-de-ciudad-inteligente-de-las-rozas/ 9 | comments: 10 | startDate: 1-1-2022 11 | -------------------------------------------------------------------------------- /CrowdFlowObserved/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/CrowdFlowObserved/SmartDataModelBadge.png -------------------------------------------------------------------------------- /CrowdFlowObserved/code/README.md: -------------------------------------------------------------------------------- 1 | # CrowdFlowObserved 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_CrowdFlowObserved.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/CrowdFlowObserved/code/code_for_using_dataModel.Transportation_CrowdFlowObserved.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /CrowdFlowObserved/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:CrowdFlowObserved:Valladolid_1", 3 | "type": "CrowdFlowObserved", 4 | "dateObserved": { 5 | "type": "DateTime", 6 | "value": "2018-08-07T11:10:00" 7 | }, 8 | "direction": { 9 | "type": "Text", 10 | "value": "inbound" 11 | }, 12 | "dateObservedFrom": { 13 | "type": "DateTime", 14 | "value": "2018-08-07T11:10:00Z" 15 | }, 16 | "peopleCount": { 17 | "type": "Number", 18 | "value": 100 19 | }, 20 | "averageHeadwayTime": { 21 | "type": "Number", 22 | "value": 5 23 | }, 24 | "dateObservedTo": { 25 | "type": "DateTime", 26 | "value": "2018-08-07T11:15:00Z" 27 | }, 28 | "location": { 29 | "type": "geo:json", 30 | "value": { 31 | "type": "LineString", 32 | "coordinates": [ 33 | [ 34 | -4.73735395519672, 35 | 41.6538181849672 36 | ], 37 | [ 38 | -4.73414858659993, 39 | 41.6600594193478 40 | ], 41 | [ 42 | -4.73447575302641, 43 | 41.659585195093 44 | ] 45 | ] 46 | } 47 | }, 48 | "congested": { 49 | "type": "Boolean", 50 | "value": false 51 | } 52 | } -------------------------------------------------------------------------------- /CrowdFlowObserved/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dateObserved__type_", "dateObserved__value_", "direction__type_", "direction__value_", "dateObservedFrom__type_", "dateObservedFrom__value_", "peopleCount__type_", "peopleCount__value_", "averageHeadwayTime__type_", "averageHeadwayTime__value_", "dateObservedTo__type_", "dateObservedTo__value_", "location__type_", "location__value__type_", "location__value__coordinates__0__0_", "location__value__coordinates__0__1_", "location__value__coordinates__1__0_", "location__value__coordinates__1__1_", "location__value__coordinates__2__0_", "location__value__coordinates__2__1_", "congested__type_", "congested__value_" 2 | "urn:ngsi-ld:CrowdFlowObserved:Valladolid_1", "CrowdFlowObserved", "DateTime", "2018-08-07T11:10:00", "Text", "inbound", "DateTime", "2018-08-07T11:10:00Z", "Number", "100", "Number", "5", "DateTime", "2018-08-07T11:15:00Z", "geo:json", "LineString", "-4.73735395519672", "41.6538181849672", "-4.73414858659993", "41.6600594193478", "-4.73447575302641", "41.659585195093", "Boolean", "False" -------------------------------------------------------------------------------- /CrowdFlowObserved/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:CrowdFlowObserved:Valladolid_1", 3 | "type": "CrowdFlowObserved", 4 | "averageHeadwayTime": { 5 | "type": "Property", 6 | "value": 5 7 | }, 8 | "congested": { 9 | "type": "Property", 10 | "value": false 11 | }, 12 | "dateObserved": { 13 | "type": "Property", 14 | "value": { 15 | "@type": "DateTime", 16 | "@value": "2018-08-07T11:10:00" 17 | } 18 | }, 19 | "dateObservedFrom": { 20 | "type": "Property", 21 | "value": { 22 | "@type": "DateTime", 23 | "@value": "2018-08-07T11:10:00Z" 24 | } 25 | }, 26 | "dateObservedTo": { 27 | "type": "Property", 28 | "value": { 29 | "@type": "DateTime", 30 | "@value": "2018-08-07T11:15:00Z" 31 | } 32 | }, 33 | "direction": { 34 | "type": "Property", 35 | "value": "inbound" 36 | }, 37 | "location": { 38 | "type": "GeoProperty", 39 | "value": { 40 | "type": "LineString", 41 | "coordinates": [ 42 | [ 43 | -4.73735395519672, 44 | 41.6538181849672 45 | ], 46 | [ 47 | -4.73414858659993, 48 | 41.6600594193478 49 | ], 50 | [ 51 | -4.73447575302641, 52 | 41.659585195093 53 | ] 54 | ] 55 | } 56 | }, 57 | "peopleCount": { 58 | "type": "Property", 59 | "value": 100 60 | }, 61 | "@context": [ 62 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 63 | ] 64 | } 65 | -------------------------------------------------------------------------------- /CrowdFlowObserved/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "averageHeadwayTime__type_", "averageHeadwayTime__value_", "congested__type_", "congested__value_", "dateObserved__type_", "dateObserved__value__@type_", "dateObserved__value__@value_", "dateObservedFrom__type_", "dateObservedFrom__value__@type_", "dateObservedFrom__value__@value_", "dateObservedTo__type_", "dateObservedTo__value__@type_", "dateObservedTo__value__@value_", "direction__type_", "direction__value_", "location__type_", "location__value__type_", "location__value__coordinates__0__0_", "location__value__coordinates__0__1_", "location__value__coordinates__1__0_", "location__value__coordinates__1__1_", "location__value__coordinates__2__0_", "location__value__coordinates__2__1_", "peopleCount__type_", "peopleCount__value_", "@context__0_" 2 | "urn:ngsi-ld:CrowdFlowObserved:Valladolid_1", "CrowdFlowObserved", "Property", "5", "Property", "False", "Property", "DateTime", "2018-08-07T11:10:00", "Property", "DateTime", "2018-08-07T11:10:00Z", "Property", "DateTime", "2018-08-07T11:15:00Z", "Property", "inbound", "GeoProperty", "LineString", "-4.73735395519672", "41.6538181849672", "-4.73414858659993", "41.6600594193478", "-4.73447575302641", "41.659585195093", "Property", "100", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /CrowdFlowObserved/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:CrowdFlowObserved:Valladolid_1", 3 | "type": "CrowdFlowObserved", 4 | "dateObserved": "2018-08-07T11:10:00/2018-08-07T11:15:00", 5 | "dateObservedFrom": "2018-08-07T11:10:00Z", 6 | "dateObservedTo": "2018-08-07T11:15:00Z", 7 | "peopleCount": 100, 8 | "averageHeadwayTime": 5, 9 | "congested": false, 10 | "direction": "inbound", 11 | "location": { 12 | "type": "LineString", 13 | "coordinates": [ 14 | [ 15 | -4.73735395519672, 16 | 41.6538181849672 17 | ], 18 | [ 19 | -4.73414858659993, 20 | 41.6600594193478 21 | ], 22 | [ 23 | -4.73447575302641, 24 | 41.659585195093 25 | ] 26 | ] 27 | } 28 | } -------------------------------------------------------------------------------- /CrowdFlowObserved/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dateObserved_", "dateObservedFrom_", "dateObservedTo_", "peopleCount_", "averageHeadwayTime_", "congested_", "direction_", "location__type_", "location__coordinates__0__0_", "location__coordinates__0__1_", "location__coordinates__1__0_", "location__coordinates__1__1_", "location__coordinates__2__0_", "location__coordinates__2__1_" 2 | "urn:ngsi-ld:CrowdFlowObserved:Valladolid_1", "CrowdFlowObserved", "2018-08-07T11:10:00/2018-08-07T11:15:00", "2018-08-07T11:10:00Z", "2018-08-07T11:15:00Z", "100", "5", "False", "inbound", "LineString", "-4.73735395519672", "41.6538181849672", "-4.73414858659993", "41.6600594193478", "-4.73447575302641", "41.659585195093" -------------------------------------------------------------------------------- /CrowdFlowObserved/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:CrowdFlowObserved:Valladolid_1", 3 | "type": "CrowdFlowObserved", 4 | "averageHeadwayTime": 5, 5 | "congested": false, 6 | "dateObserved": "2018-08-07T11:10:00/2018-08-07T11:15:00", 7 | "dateObservedFrom": "2018-08-07T11:10:00Z", 8 | "dateObservedTo": "2018-08-07T11:15:00Z", 9 | "direction": "inbound", 10 | "location": { 11 | "coordinates": [ 12 | [ 13 | -4.73735395519672, 14 | 41.6538181849672 15 | ], 16 | [ 17 | -4.73414858659993, 18 | 41.6600594193478 19 | ], 20 | [ 21 | -4.73447575302641, 22 | 41.659585195093 23 | ] 24 | ], 25 | "type": "LineString" 26 | }, 27 | "peopleCount": 100, 28 | "@context": [ 29 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 30 | ] 31 | } -------------------------------------------------------------------------------- /CrowdFlowObserved/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "averageHeadwayTime_", "congested_", "dateObserved_", "dateObservedFrom_", "dateObservedTo_", "direction_", "location__coordinates__0__0_", "location__coordinates__0__1_", "location__coordinates__1__0_", "location__coordinates__1__1_", "location__coordinates__2__0_", "location__coordinates__2__1_", "location__type_", "peopleCount_", "@context__0_" 2 | "urn:ngsi-ld:CrowdFlowObserved:Valladolid_1", "CrowdFlowObserved", "5", "False", "2018-08-07T11:10:00/2018-08-07T11:15:00", "2018-08-07T11:10:00Z", "2018-08-07T11:15:00Z", "inbound", "-4.73735395519672", "41.6538181849672", "-4.73414858659993", "41.6600594193478", "-4.73447575302641", "41.659585195093", "LineString", "100", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /CrowdFlowObserved/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | An observation related to the movement of people at a certain place and time. 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /CrowdFlowObserved/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model CrowdFlowObserved of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE direction_type AS ENUM ('inbound','outbound');CREATE TYPE CrowdFlowObserved_type AS ENUM ('CrowdFlowObserved'); 3 | CREATE TABLE CrowdFlowObserved (address JSON, alternateName TEXT, areaServed TEXT, averageCrowdSpeed NUMERIC, averageHeadwayTime NUMERIC, congested BOOLEAN, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, dateObserved TEXT, dateObservedFrom TIMESTAMP, dateObservedTo TIMESTAMP, description TEXT, direction direction_type, id TEXT PRIMARY KEY, location JSON, name TEXT, occupancy NUMERIC, owner JSON, peopleCount NUMERIC, seeAlso JSON, source TEXT, type CrowdFlowObserved_type); -------------------------------------------------------------------------------- /CrowdFlowObserved/swagger.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | # Copyleft (c) 2022 Contributors to Smart Data Models initiative 3 | # 4 | 5 | 6 | components: 7 | schemas: 8 | CrowdFlowObserved: 9 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/CrowdFlowObserved/model.yaml#/CrowdFlowObserved" 10 | info: 11 | description: | 12 | CrowdFlowObserved 13 | title: CrowdFlowObserved 14 | version: "0.0.2" 15 | openapi: "3.0.0" 16 | 17 | paths: 18 | /ngsi-ld/v1/entities: 19 | get: 20 | description: "Retrieve a set of entities which matches a specific query from an NGSI-LD system" 21 | parameters: 22 | - 23 | in: query 24 | name: type 25 | required: true 26 | schema: 27 | enum: 28 | - CrowdFlowObserved 29 | type: string 30 | responses: 31 | ? "200" 32 | : 33 | content: 34 | application/ld+json: 35 | examples: 36 | keyvalues: 37 | summary: "Key-Values Pairs" 38 | value: 39 | - 40 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/CrowdFlowObserved/examples/example.json" 41 | normalized: 42 | summary: "Normalized NGSI-LD" 43 | value: 44 | - 45 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/CrowdFlowObserved/examples/example-normalized.jsonld" 46 | description: OK 47 | tags: 48 | - ngsi-ld 49 | tags: 50 | - 51 | description: "NGSI-LD Linked-data Format" 52 | name: ngsi-ld 53 | -------------------------------------------------------------------------------- /EVChargingStation/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model EVChargingStation of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: IUDX 5 | description: A Data Model for Electric Vehicle Charging Stations. 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /EVChargingStation/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/EVChargingStation/SmartDataModelBadge.png -------------------------------------------------------------------------------- /EVChargingStation/code/README.md: -------------------------------------------------------------------------------- 1 | # EVChargingStation 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_EVChargingStation.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/EVChargingStation/code/code_for_using_dataModel.Transportation_EVChargingStation.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /EVChargingStation/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:EVChargingStation:ValladolI+D_Covaresa", 3 | "type": "EVChargingStation", 4 | "name": "Agencia de Innovaci\u00f3n", 5 | "location": { 6 | "coordinates": [ 7 | -4.747901, 8 | 41.618265 9 | ], 10 | "type": "Point" 11 | }, 12 | "capacity": 2, 13 | "socketType": [ 14 | "Wall_Euro" 15 | ], 16 | "address": { 17 | "streetAddress": "Paseo de Zorrilla, 191", 18 | "addressLocality": "Valladolid", 19 | "addressCountry": "Espa\u00f1a" 20 | }, 21 | "contactPoint": { 22 | "email": "vehiculoelectrico@ava.es" 23 | }, 24 | "operator": "Iberdrola", 25 | "allowedVehicleType": [ 26 | "car" 27 | ], 28 | "chargeType": [ 29 | "free" 30 | ], 31 | "source": "https://openchargemap.org/", 32 | "powerConsumption": 10.0, 33 | "chargingUnitId": "PZEV01-DeltaBharatAC001-SCTLGandhiPark001", 34 | "transactionId": "84068784", 35 | "transactionType": "RFID", 36 | "stationName": "SmartCityTvmGandhiParkOne", 37 | "amountCollected": 0.08, 38 | "taxAmountCollected": 0.0, 39 | "endDateTime": "2022-06-28T20:28:41+05:30", 40 | "startDateTime": "2022-06-28T20:27:27+05:30", 41 | "vehicleType": "e-motorcycle", 42 | "observationDateTime": "2022-06-28T20:27:29+05:30" 43 | } -------------------------------------------------------------------------------- /EVChargingStation/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name_", "location__coordinates__0_", "location__coordinates__1_", "location__type_", "capacity_", "socketType__0_", "address__streetAddress_", "address__addressLocality_", "address__addressCountry_", "contactPoint__email_", "operator_", "allowedVehicleType__0_", "chargeType__0_", "source_", "powerConsumption_", "chargingUnitId_", "transactionId_", "transactionType_", "stationName_", "amountCollected_", "taxAmountCollected_", "endDateTime_", "startDateTime_", "vehicleType_", "observationDateTime_" 2 | "urn:ngsi-ld:EVChargingStation:ValladolI+D_Covaresa", "EVChargingStation", "Agencia de Innovación", "-4.747901", "41.618265", "Point", "2", "Wall_Euro", "Paseo de Zorrilla, 191", "Valladolid", "España", "vehiculoelectrico@ava.es", "Iberdrola", "car", "free", "https://openchargemap.org/", "10.0", "PZEV01-DeltaBharatAC001-SCTLGandhiPark001", "84068784", "RFID", "SmartCityTvmGandhiParkOne", "0.08", "0.0", "2022-06-28T20:28:41+05:30", "2022-06-28T20:27:27+05:30", "e-motorcycle", "2022-06-28T20:27:29+05:30" -------------------------------------------------------------------------------- /EVChargingStation/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:EVChargingStation:ValladolI+D_Covaresa", 3 | "type": "EVChargingStation", 4 | "name": "Agencia de Innovaci\u00f3n", 5 | "location": { 6 | "coordinates": [ 7 | -4.747901, 8 | 41.618265 9 | ], 10 | "type": "Point" 11 | }, 12 | "capacity": 2, 13 | "socketType": [ 14 | "Wall_Euro" 15 | ], 16 | "address": { 17 | "streetAddress": "Paseo de Zorrilla, 191", 18 | "addressLocality": "Valladolid", 19 | "addressCountry": "Espa\u00f1a" 20 | }, 21 | "contactPoint": { 22 | "email": "vehiculoelectrico@ava.es" 23 | }, 24 | "operator": "Iberdrola", 25 | "allowedVehicleType": [ 26 | "car" 27 | ], 28 | "chargeType": [ 29 | "free" 30 | ], 31 | "source": "https://openchargemap.org/", 32 | "powerConsumption": 10.0, 33 | "chargingUnitId": "PZEV01-DeltaBharatAC001-SCTLGandhiPark001", 34 | "transactionId": "84068784", 35 | "transactionType": "RFID", 36 | "stationName": "SmartCityTvmGandhiParkOne", 37 | "amountCollected": 0.08, 38 | "taxAmountCollected": 0.0, 39 | "endDateTime": "2022-06-28T20:28:41+05:30", 40 | "startDateTime": "2022-06-28T20:27:27+05:30", 41 | "vehicleType": "e-motorcycle", 42 | "observationDateTime": "2022-06-28T20:27:29+05:30", 43 | "@context": [ 44 | "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld", 45 | "iudx:EVChargingStation" 46 | ] 47 | } -------------------------------------------------------------------------------- /EVChargingStation/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name_", "location__coordinates__0_", "location__coordinates__1_", "location__type_", "capacity_", "socketType__0_", "address__streetAddress_", "address__addressLocality_", "address__addressCountry_", "contactPoint__email_", "operator_", "allowedVehicleType__0_", "chargeType__0_", "source_", "powerConsumption_", "chargingUnitId_", "transactionId_", "transactionType_", "stationName_", "amountCollected_", "taxAmountCollected_", "endDateTime_", "startDateTime_", "vehicleType_", "observationDateTime_", "@context__0_", "@context__1_" 2 | "urn:ngsi-ld:EVChargingStation:ValladolI+D_Covaresa", "EVChargingStation", "Agencia de Innovación", "-4.747901", "41.618265", "Point", "2", "Wall_Euro", "Paseo de Zorrilla, 191", "Valladolid", "España", "vehiculoelectrico@ava.es", "Iberdrola", "car", "free", "https://openchargemap.org/", "10.0", "PZEV01-DeltaBharatAC001-SCTLGandhiPark001", "84068784", "RFID", "SmartCityTvmGandhiParkOne", "0.08", "0.0", "2022-06-28T20:28:41+05:30", "2022-06-28T20:27:27+05:30", "e-motorcycle", "2022-06-28T20:27:29+05:30", "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld", "iudx:EVChargingStation" -------------------------------------------------------------------------------- /EVChargingStation/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | A public charging station supplying energy to electrical vehicles. The charge time depends on the maximum power output of the station, the number of vehicles currently charging and the current battery level. 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /EVChargingStation/swagger.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | # Copyleft (c) 2022 Contributors to Smart Data Models initiative 3 | # 4 | 5 | 6 | components: 7 | schemas: 8 | EVChargingStation: 9 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/EVChargingStation/model.yaml#/EVChargingStation" 10 | info: 11 | description: | 12 | EV Charging Station 13 | title: EVChargingStation 14 | version: "0.1.0" 15 | openapi: "3.0.0" 16 | 17 | paths: 18 | /ngsi-ld/v1/entities: 19 | get: 20 | description: "Retrieve a set of entities which matches a specific query from an NGSI-LD system" 21 | parameters: 22 | - 23 | in: query 24 | name: type 25 | required: true 26 | schema: 27 | enum: 28 | - EVChargingStation 29 | type: string 30 | responses: 31 | ? "200" 32 | : 33 | content: 34 | application/ld+json: 35 | examples: 36 | keyvalues: 37 | summary: "Key-Values Pairs" 38 | value: 39 | - 40 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/EVChargingStation/examples/example.json" 41 | normalized: 42 | summary: "Normalized NGSI-LD" 43 | value: 44 | - 45 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/EVChargingStation/examples/example-normalized.jsonld" 46 | description: OK 47 | tags: 48 | - ngsi-ld 49 | tags: 50 | - 51 | description: "NGSI-LD Linked-data Format" 52 | name: ngsi-ld 53 | -------------------------------------------------------------------------------- /FareCollectionSystem/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model FareCollectionSystem of the Subject Transportation. 2 | currentAdopters: 3 | - 4 | adopter: IUDX 5 | description: A public transit fare collection system Data Model. 6 | mail: 7 | organization: IUDX 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /FareCollectionSystem/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/FareCollectionSystem/SmartDataModelBadge.png -------------------------------------------------------------------------------- /FareCollectionSystem/code/README.md: -------------------------------------------------------------------------------- 1 | # FareCollectionSystem 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_FareCollectionSystem.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/FareCollectionSystem/code/code_for_using_dataModel.Transportation_FareCollectionSystem.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /FareCollectionSystem/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | 3 | notesMiddle: 4 | 5 | notesFooter: 6 | 7 | notesReadme: 8 | 9 | -------------------------------------------------------------------------------- /FareCollectionSystem/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model FareCollectionSystem of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE equipmentTypeCode_type AS ENUM ('1B','42','2','8','41');CREATE TYPE occupancyLevel_type AS ENUM ('Red','Yellow','Green');CREATE TYPE FareCollectionSystem_type AS ENUM ('FareCollectionSystem'); 3 | CREATE TABLE FareCollectionSystem (address JSON, alternateName TEXT, areaServed TEXT, cardId TEXT, currentTripCount NUMERIC, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, destinationStopCategory TEXT, destinationStopId TEXT, destinationStopName TEXT, direction_id NUMERIC, entryAreaCode TEXT, equipmentCompanyCode TEXT, equipmentId TEXT, equipmentSequenceNumber NUMERIC, equipmentStopId TEXT, equipmentType TEXT, equipmentTypeCode equipmentTypeCode_type, exitAreaCode TEXT, fareForAdult NUMERIC, fareForChild NUMERIC, id TEXT PRIMARY KEY, location JSON, name TEXT, observationDateTime TIMESTAMP, occupancyLevel occupancyLevel_type, originDestinationCode TEXT, originStopCategory TEXT, originStopId TEXT, originStopName TEXT, owner JSON, passengerCount NUMERIC, route_id TEXT, seeAlso JSON, shiftOfOperation TEXT, source TEXT, stage NUMERIC, ticketTypeCode TEXT, transactionDateTime TIMESTAMP, transactionType TEXT, transactionTypeDescription TEXT, transactionVehicleNum NUMERIC, travelDistance NUMERIC, trip_id TEXT, type FareCollectionSystem_type, vehicle_label TEXT); -------------------------------------------------------------------------------- /FareCollectionSystem/swagger.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | # Copyleft (c) 2022 Contributors to Smart Data Models initiative 3 | # 4 | 5 | 6 | components: 7 | schemas: 8 | FareCollectionSystem: 9 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/FareCollectionSystem/model.yaml#/FareCollectionSystem" 10 | info: 11 | description: | 12 | A public transit fare collection system Data Model 13 | title: FareCollectionSystem 14 | version: "0.0.1" 15 | openapi: "3.0.0" 16 | 17 | paths: 18 | /ngsi-ld/v1/entities: 19 | get: 20 | description: "Retrieve a set of entities which matches a specific query from an NGSI-LD system" 21 | parameters: 22 | - 23 | in: query 24 | name: type 25 | required: true 26 | schema: 27 | enum: 28 | - FareCollectionSystem 29 | type: string 30 | responses: 31 | ? "200" 32 | : 33 | content: 34 | application/ld+json: 35 | examples: 36 | keyvalues: 37 | summary: "Key-Values Pairs" 38 | value: 39 | - 40 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/FareCollectionSystem/examples/example.json" 41 | normalized: 42 | summary: "Normalized NGSI-LD" 43 | value: 44 | - 45 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/FareCollectionSystem/examples/example-normalized.jsonld" 46 | description: OK 47 | tags: 48 | - ngsi-ld 49 | tags: 50 | - 51 | description: "NGSI-LD Linked-data Format" 52 | name: ngsi-ld 53 | -------------------------------------------------------------------------------- /FleetVehicle/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model FleetVehicle of the Subject Transportation. 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /FleetVehicle/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/FleetVehicle/SmartDataModelBadge.png -------------------------------------------------------------------------------- /FleetVehicle/code/README.md: -------------------------------------------------------------------------------- 1 | # FleetVehicle 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_FleetVehicle.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/FleetVehicle/code/code_for_using_dataModel.Transportation_FleetVehicle.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /FleetVehicle/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:FleetVehicle:630b8818-5aa2-11e8-91c6-bf6b90c0ad02", 3 | "type": "FleetVehicle", 4 | "source": { 5 | "type": "Text", 6 | "value": "https://source.example.com" 7 | }, 8 | "dataProvider": { 9 | "type": "Text", 10 | "value": "https://provider.example.com" 11 | }, 12 | "vehicle": { 13 | "type": "Text", 14 | "value": "urn:ngsi-ld:Vehicle:76a67054-5aa2-11e8-b0ee-43cfe58d3cd1" 15 | }, 16 | "fleetVehicleType": { 17 | "type": "Text", 18 | "value": "Ambulance" 19 | }, 20 | "operatingCompany": { 21 | "type": "Text", 22 | "value": "NHS" 23 | }, 24 | "operator": { 25 | "type": "Text", 26 | "value": "urn:ngsi-ld:Person:fe018d4e-46f8-11e8-ae6b-df5577f85836" 27 | } 28 | } -------------------------------------------------------------------------------- /FleetVehicle/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "source__type_", "source__value_", "dataProvider__type_", "dataProvider__value_", "vehicle__type_", "vehicle__value_", "fleetVehicleType__type_", "fleetVehicleType__value_", "operatingCompany__type_", "operatingCompany__value_", "operator__type_", "operator__value_" 2 | "urn:ngsi-ld:FleetVehicle:630b8818-5aa2-11e8-91c6-bf6b90c0ad02", "FleetVehicle", "Text", "https://source.example.com", "Text", "https://provider.example.com", "Text", "urn:ngsi-ld:Vehicle:76a67054-5aa2-11e8-b0ee-43cfe58d3cd1", "Text", "Ambulance", "Text", "NHS", "Text", "urn:ngsi-ld:Person:fe018d4e-46f8-11e8-ae6b-df5577f85836" -------------------------------------------------------------------------------- /FleetVehicle/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:FleetVehicle:630b8818-5aa2-11e8-91c6-bf6b90c0ad02", 3 | "type": "FleetVehicle", 4 | "dataProvider": { 5 | "type": "Property", 6 | "value": "https://provider.example.com" 7 | }, 8 | "fleetVehicleType": { 9 | "type": "Property", 10 | "value": "Ambulance" 11 | }, 12 | "operatingCompany": { 13 | "type": "Property", 14 | "value": { 15 | "@type": "https://schema.org/Organization", 16 | "@value": "NHS" 17 | } 18 | }, 19 | "operator": { 20 | "type": "Relationship", 21 | "object": "urn:ngsi-ld:Person:fe018d4e-46f8-11e8-ae6b-df5577f85836" 22 | }, 23 | "source": { 24 | "type": "Property", 25 | "value": "https://source.example.com" 26 | }, 27 | "vehicle": { 28 | "type": "Relationship", 29 | "object": "urn:ngsi-ld:Vehicle:76a67054-5aa2-11e8-b0ee-43cfe58d3cd1" 30 | }, 31 | "@context": [ 32 | "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" 33 | ] 34 | } -------------------------------------------------------------------------------- /FleetVehicle/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dataProvider__type_", "dataProvider__value_", "fleetVehicleType__type_", "fleetVehicleType__value_", "operatingCompany__type_", "operatingCompany__value__@type_", "operatingCompany__value__@value_", "operator__type_", "operator__object_", "source__type_", "source__value_", "vehicle__type_", "vehicle__object_", "@context__0_" 2 | "urn:ngsi-ld:FleetVehicle:630b8818-5aa2-11e8-91c6-bf6b90c0ad02", "FleetVehicle", "Property", "https://provider.example.com", "Property", "Ambulance", "Property", "https://schema.org/Organization", "NHS", "Relationship", "urn:ngsi-ld:Person:fe018d4e-46f8-11e8-ae6b-df5577f85836", "Property", "https://source.example.com", "Relationship", "urn:ngsi-ld:Vehicle:76a67054-5aa2-11e8-b0ee-43cfe58d3cd1", "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" -------------------------------------------------------------------------------- /FleetVehicle/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:FleetVehicle:630b8818-5aa2-11e8-91c6-bf6b90c0ad02", 3 | "type": "FleetVehicle", 4 | "source": "https://source.example.com", 5 | "dataProvider": "https://provider.example.com", 6 | "vehicle": "urn:ngsi-ld:Vehicle:76a67054-5aa2-11e8-b0ee-43cfe58d3cd1", 7 | "fleetVehicleType": "Ambulance", 8 | "operatingCompany": "NHS", 9 | "operator": "urn:ngsi-ld:Person:fe018d4e-46f8-11e8-ae6b-df5577f85836" 10 | } -------------------------------------------------------------------------------- /FleetVehicle/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "source_", "dataProvider_", "vehicle_", "fleetVehicleType_", "operatingCompany_", "operator_" 2 | "urn:ngsi-ld:FleetVehicle:630b8818-5aa2-11e8-91c6-bf6b90c0ad02", "FleetVehicle", "https://source.example.com", "https://provider.example.com", "urn:ngsi-ld:Vehicle:76a67054-5aa2-11e8-b0ee-43cfe58d3cd1", "Ambulance", "NHS", "urn:ngsi-ld:Person:fe018d4e-46f8-11e8-ae6b-df5577f85836" -------------------------------------------------------------------------------- /FleetVehicle/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:FleetVehicle:630b8818-5aa2-11e8-91c6-bf6b90c0ad02", 3 | "type": "FleetVehicle", 4 | "dataProvider": "https://provider.example.com", 5 | "fleetVehicleType": "Ambulance", 6 | "operatingCompany": "NHS", 7 | "operator": "urn:ngsi-ld:Person:fe018d4e-46f8-11e8-ae6b-df5577f85836", 8 | "source": "https://source.example.com", 9 | "vehicle": "urn:ngsi-ld:Vehicle:76a67054-5aa2-11e8-b0ee-43cfe58d3cd1", 10 | "@context": [ 11 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 12 | ] 13 | } -------------------------------------------------------------------------------- /FleetVehicle/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dataProvider_", "fleetVehicleType_", "operatingCompany_", "operator_", "source_", "vehicle_", "@context__0_" 2 | "urn:ngsi-ld:FleetVehicle:630b8818-5aa2-11e8-91c6-bf6b90c0ad02", "FleetVehicle", "https://provider.example.com", "Ambulance", "NHS", "urn:ngsi-ld:Person:fe018d4e-46f8-11e8-ae6b-df5577f85836", "https://source.example.com", "urn:ngsi-ld:Vehicle:76a67054-5aa2-11e8-b0ee-43cfe58d3cd1", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /FleetVehicle/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | This data model comes from the original project GSMA IoT project, https://www.gsma.com/iot/iot-big-data/. There are some minor adaptations to meet requirements of smart data models. 3 | notesMiddle: 4 | 5 | notesFooter: 6 | 7 | notesReadme: 8 | This data model comes from the original project GSMA IoT project, https://www.gsma.com/iot/iot-big-data/. -------------------------------------------------------------------------------- /FleetVehicle/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model FleetVehicle of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE FleetVehicle_type AS ENUM ('FleetVehicle'); 3 | CREATE TABLE FleetVehicle (address JSON, alternateName TEXT, areaServed TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, fleetVehicleType TEXT, id TEXT PRIMARY KEY, location JSON, name TEXT, operatingCompany TEXT, owner JSON, seeAlso JSON, source TEXT, type FleetVehicle_type); -------------------------------------------------------------------------------- /FleetVehicleOperation/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model FleetVehicleOperation of the Subject Transportation. 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /FleetVehicleOperation/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/FleetVehicleOperation/SmartDataModelBadge.png -------------------------------------------------------------------------------- /FleetVehicleOperation/code/README.md: -------------------------------------------------------------------------------- 1 | # FleetVehicleOperation 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_FleetVehicleOperation.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/FleetVehicleOperation/code/code_for_using_dataModel.Transportation_FleetVehicleOperation.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /FleetVehicleOperation/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:FleetVehicleOperation:8e876a60-5aa3-11e8-b350-d7b51a09fb6c", 3 | "type": "FleetVehicleOperation", 4 | "source": { 5 | "type": "Text", 6 | "value": "https://source.example.com" 7 | }, 8 | "dataProvider": { 9 | "type": "Text", 10 | "value": "https://provider.example.com" 11 | }, 12 | "fleetVehicle": { 13 | "type": "Text", 14 | "value": "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f" 15 | }, 16 | "fleetVehicleStatus": { 17 | "type": "Text", 18 | "value": "urn:ngsi-ld:FleetVehicleStatus:0284e0dc-5aa4-11e8-97e6-2351fc70c286" 19 | }, 20 | "initiatingLocation": { 21 | "type": "geo:json", 22 | "value": { 23 | "type": "Point", 24 | "coordinates": [ 25 | -104.99404, 26 | 39.75621 27 | ] 28 | } 29 | }, 30 | "startedAt": { 31 | "type": "DateTime", 32 | "value": "2016-08-22T10:18:16Z" 33 | }, 34 | "endedAt": { 35 | "type": "DateTime", 36 | "value": "2016-08-28T10:18:16Z" 37 | }, 38 | "operationType": { 39 | "type": "Text", 40 | "value": "Patient transportation" 41 | }, 42 | "description": { 43 | "type": "Text", 44 | "value": "An emergency transportation of a 3 year old boy" 45 | }, 46 | "result": { 47 | "type": "Text", 48 | "value": "Completed" 49 | }, 50 | "responseTime": { 51 | "type": "Number", 52 | "value": 2500 53 | }, 54 | "transportTime": { 55 | "type": "Number", 56 | "value": 1220 57 | } 58 | } -------------------------------------------------------------------------------- /FleetVehicleOperation/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "source__type_", "source__value_", "dataProvider__type_", "dataProvider__value_", "fleetVehicle__type_", "fleetVehicle__value_", "fleetVehicleStatus__type_", "fleetVehicleStatus__value_", "initiatingLocation__type_", "initiatingLocation__value__type_", "initiatingLocation__value__coordinates__0_", "initiatingLocation__value__coordinates__1_", "startedAt__type_", "startedAt__value_", "endedAt__type_", "endedAt__value_", "operationType__type_", "operationType__value_", "description__type_", "description__value_", "result__type_", "result__value_", "responseTime__type_", "responseTime__value_", "transportTime__type_", "transportTime__value_" 2 | "urn:ngsi-ld:FleetVehicleOperation:8e876a60-5aa3-11e8-b350-d7b51a09fb6c", "FleetVehicleOperation", "Text", "https://source.example.com", "Text", "https://provider.example.com", "Text", "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", "Text", "urn:ngsi-ld:FleetVehicleStatus:0284e0dc-5aa4-11e8-97e6-2351fc70c286", "geo:json", "Point", "-104.99404", "39.75621", "DateTime", "2016-08-22T10:18:16Z", "DateTime", "2016-08-28T10:18:16Z", "Text", "Patient transportation", "Text", "An emergency transportation of a 3 year old boy", "Text", "Completed", "Number", "2500", "Number", "1220" -------------------------------------------------------------------------------- /FleetVehicleOperation/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dataProvider__type_", "dataProvider__value_", "description__type_", "description__value_", "endedAt__type_", "endedAt__value__@type_", "endedAt__value__@value_", "fleetVehicle__type_", "fleetVehicle__object_", "fleetVehicleStatus__type_", "fleetVehicleStatus__object_", "initiatingLocation__type_", "initiatingLocation__value__type_", "initiatingLocation__value__coordinates__0_", "initiatingLocation__value__coordinates__1_", "operationType__type_", "operationType__value_", "responseTime__type_", "responseTime__value_", "responseTime__unitCode_", "responseTime__observedAt_", "result__type_", "result__value_", "source__type_", "source__value_", "startedAt__type_", "startedAt__value__@type_", "startedAt__value__@value_", "transportTime__type_", "transportTime__value_", "transportTime__unitCode_", "transportTime__observedAt_", "@context__0_" 2 | "urn:ngsi-ld:FleetVehicleOperation:8e876a60-5aa3-11e8-b350-d7b51a09fb6c", "FleetVehicleOperation", "Property", "https://provider.example.com", "Property", "An emergency transportation of a 3 year old boy", "Property", "DateTime", "2016-08-28T10:18:16Z", "Relationship", "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", "Relationship", "urn:ngsi-ld:FleetVehicleStatus:0284e0dc-5aa4-11e8-97e6-2351fc70c286", "GeoProperty", "Point", "-104.99404", "39.75621", "Property", "Patient transportation", "Property", "2500", "SEC", "2016-08-28T10:18:16Z", "Property", "Completed", "Property", "https://source.example.com", "Property", "DateTime", "2016-08-22T10:18:16Z", "Property", "1220", "SEC", "2016-08-28T10:18:16Z", "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" -------------------------------------------------------------------------------- /FleetVehicleOperation/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:FleetVehicleOperation:8e876a60-5aa3-11e8-b350-d7b51a09fb6c", 3 | "type": "FleetVehicleOperation", 4 | "source": "https://source.example.com", 5 | "dataProvider": "https://provider.example.com", 6 | "fleetVehicle": "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", 7 | "fleetVehicleStatus": "urn:ngsi-ld:FleetVehicleStatus:0284e0dc-5aa4-11e8-97e6-2351fc70c286", 8 | "initiatingLocation": { 9 | "type": "Point", 10 | "coordinates": [ 11 | -104.99404, 12 | 39.75621 13 | ] 14 | }, 15 | "startedAt": "2016-08-22T10:18:16Z", 16 | "endedAt": "2016-08-28T10:18:16Z", 17 | "operationType": "Patient transportation", 18 | "description": "An emergency transportation of a 3 year old boy", 19 | "result": "Completed", 20 | "responseTime": 2500, 21 | "transportTime": 1220 22 | } -------------------------------------------------------------------------------- /FleetVehicleOperation/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "source_", "dataProvider_", "fleetVehicle_", "fleetVehicleStatus_", "initiatingLocation__type_", "initiatingLocation__coordinates__0_", "initiatingLocation__coordinates__1_", "startedAt_", "endedAt_", "operationType_", "description_", "result_", "responseTime_", "transportTime_" 2 | "urn:ngsi-ld:FleetVehicleOperation:8e876a60-5aa3-11e8-b350-d7b51a09fb6c", "FleetVehicleOperation", "https://source.example.com", "https://provider.example.com", "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", "urn:ngsi-ld:FleetVehicleStatus:0284e0dc-5aa4-11e8-97e6-2351fc70c286", "Point", "-104.99404", "39.75621", "2016-08-22T10:18:16Z", "2016-08-28T10:18:16Z", "Patient transportation", "An emergency transportation of a 3 year old boy", "Completed", "2500", "1220" -------------------------------------------------------------------------------- /FleetVehicleOperation/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:FleetVehicleOperation:8e876a60-5aa3-11e8-b350-d7b51a09fb6c", 3 | "type": "FleetVehicleOperation", 4 | "dataProvider": "https://provider.example.com", 5 | "description": "An emergency transportation of a 3 year old boy", 6 | "endedAt": "2016-08-28T10:18:16Z", 7 | "fleetVehicle": "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", 8 | "fleetVehicleStatus": "urn:ngsi-ld:FleetVehicleStatus:0284e0dc-5aa4-11e8-97e6-2351fc70c286", 9 | "initiatingLocation": { 10 | "type": "Point", 11 | "coordinates": [ 12 | -104.99404, 13 | 39.75621 14 | ] 15 | }, 16 | "operationType": "Patient transportation", 17 | "responseTime": 2500, 18 | "result": "Completed", 19 | "source": "https://source.example.com", 20 | "startedAt": "2016-08-22T10:18:16Z", 21 | "transportTime": 1220, 22 | "@context": [ 23 | "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" 24 | ] 25 | } -------------------------------------------------------------------------------- /FleetVehicleOperation/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dataProvider_", "description_", "endedAt_", "fleetVehicle_", "fleetVehicleStatus_", "initiatingLocation__type_", "initiatingLocation__coordinates__0_", "initiatingLocation__coordinates__1_", "operationType_", "responseTime_", "result_", "source_", "startedAt_", "transportTime_", "@context__0_" 2 | "urn:ngsi-ld:FleetVehicleOperation:8e876a60-5aa3-11e8-b350-d7b51a09fb6c", "FleetVehicleOperation", "https://provider.example.com", "An emergency transportation of a 3 year old boy", "2016-08-28T10:18:16Z", "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", "urn:ngsi-ld:FleetVehicleStatus:0284e0dc-5aa4-11e8-97e6-2351fc70c286", "Point", "-104.99404", "39.75621", "Patient transportation", "2500", "Completed", "https://source.example.com", "2016-08-22T10:18:16Z", "1220", "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" -------------------------------------------------------------------------------- /FleetVehicleOperation/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | This data model comes from the original project GSMA IoT project, https://www.gsma.com/iot/iot-big-data/. There are some minor adaptations to meet requirements of smart data models. 3 | notesMiddle: 4 | 5 | notesFooter: 6 | 7 | notesReadme: 8 | This data model comes from the original project GSMA IoT project, https://www.gsma.com/iot/iot-big-data/. -------------------------------------------------------------------------------- /FleetVehicleOperation/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model FleetVehicleOperation of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE FleetVehicleOperation_type AS ENUM ('FleetVehicleOperation'); 3 | CREATE TABLE FleetVehicleOperation (address JSON, alternateName TEXT, areaServed TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, endedAt TIMESTAMP, id TEXT PRIMARY KEY, initiatingLocation JSON, location JSON, name TEXT, operationType TEXT, owner JSON, responseTime NUMERIC, result TEXT, seeAlso JSON, source TEXT, startedAt TIMESTAMP, transportTime NUMERIC, type FleetVehicleOperation_type); -------------------------------------------------------------------------------- /FleetVehicleStatus/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model FleetVehicleStatus of the Subject Transportation. 2 | currentAdopters: 3 | - 4 | adopter: SEDIMARK project 5 | description: Urban bike mobility planning use case in Santander 6 | mail: 7 | organization: 8 | project: https://sedimark.eu/ 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /FleetVehicleStatus/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/FleetVehicleStatus/SmartDataModelBadge.png -------------------------------------------------------------------------------- /FleetVehicleStatus/code/README.md: -------------------------------------------------------------------------------- 1 | # FleetVehicleStatus 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_FleetVehicleStatus.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/FleetVehicleStatus/code/code_for_using_dataModel.Transportation_FleetVehicleStatus.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /FleetVehicleStatus/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "battery__type_", "battery__value_", "source__type_", "source__value_", "dataProvider__type_", "dataProvider__value_", "fleetVehicle__type_", "fleetVehicle__value_", "fleetVehicleOperation__type_", "fleetVehicleOperation__value_", "restFuelAmount__type_", "restFuelAmount__value_", "lastFuellingAmount__type_", "lastFuellingAmount__value_", "currentStatus__type_", "currentStatus__value_", "currentOperative__type_", "currentOperative__value__givenName_", "currentOperative__value__jobTitle_", "speed__type_", "speed__value_", "bearing__type_", "bearing__value_", "lastKnownPosition__type_", "lastKnownPosition__value__type_", "lastKnownPosition__value__coordinates__0_", "lastKnownPosition__value__coordinates__1_", "lastKnownPositionUpdatedAt__type_", "lastKnownPositionUpdatedAt__value_", "inRestrictedArea__type_", "inRestrictedArea__value_", "mileageFromOdometer__type_", "mileageFromOdometer__value_" 2 | "urn:ngsi-ld:FleetVehicleStatus:16ea1c5c-5aa6-11e8-8144-4b82063ca31c", "FleetVehicleStatus", "Number", "0.81", "Text", "https://source.example.com", "Text", "https://provider.example.com", "Text", "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", "Text", "urn:ngsi-ld:FleetVehicleOperation:a4f0a07a-5aa6-11e8-b70f-4b9d36e53d7b", "Number", "28", "Number", "95", "Text", "finished", "StructuredValue", "John Smith", "Ambulance Operator", "Number", "60", "Number", "80", "geo:json", "Point", "-104.99404", "39.75621", "DateTime", "2016-08-28T10:18:16Z", "Boolean", "True", "Number", "18756" -------------------------------------------------------------------------------- /FleetVehicleStatus/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:FleetVehicleStatus:16ea1c5c-5aa6-11e8-8144-4b82063ca31c", 3 | "type": "FleetVehicleStatus", 4 | "battery": 0.81, 5 | "source": "https://source.example.com", 6 | "dataProvider": "https://provider.example.com", 7 | "fleetVehicle": "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", 8 | "fleetVehicleOperation": "urn:ngsi-ld:FleetVehicleOperation:a4f0a07a-5aa6-11e8-b70f-4b9d36e53d7b", 9 | "restFuelAmount": 28, 10 | "lastFuellingAmount": 95, 11 | "currentStatus": "finished", 12 | "currentOperative": { 13 | "givenName": "John Smith", 14 | "jobTitle": "Ambulance Operator" 15 | }, 16 | "speed": 60, 17 | "unitCode": "KMH", 18 | "bearing": 80, 19 | "lastKnownPosition": { 20 | "type": "Point", 21 | "coordinates": [ 22 | -104.99404, 23 | 39.75621 24 | ] 25 | }, 26 | "lastKnownPositionUpdatedAt": "2016-08-28T10:18:16Z", 27 | "inRestrictedArea": true, 28 | "mileageFromOdometer": 18756 29 | } -------------------------------------------------------------------------------- /FleetVehicleStatus/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "battery_", "source_", "dataProvider_", "fleetVehicle_", "fleetVehicleOperation_", "restFuelAmount_", "lastFuellingAmount_", "currentStatus_", "currentOperative__givenName_", "currentOperative__jobTitle_", "speed_", "unitCode_", "bearing_", "lastKnownPosition__type_", "lastKnownPosition__coordinates__0_", "lastKnownPosition__coordinates__1_", "lastKnownPositionUpdatedAt_", "inRestrictedArea_", "mileageFromOdometer_" 2 | "urn:ngsi-ld:FleetVehicleStatus:16ea1c5c-5aa6-11e8-8144-4b82063ca31c", "FleetVehicleStatus", "0.81", "https://source.example.com", "https://provider.example.com", "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", "urn:ngsi-ld:FleetVehicleOperation:a4f0a07a-5aa6-11e8-b70f-4b9d36e53d7b", "28", "95", "finished", "John Smith", "Ambulance Operator", "60", "KMH", "80", "Point", "-104.99404", "39.75621", "2016-08-28T10:18:16Z", "True", "18756" -------------------------------------------------------------------------------- /FleetVehicleStatus/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:FleetVehicleStatus:16ea1c5c-5aa6-11e8-8144-4b82063ca31c", 3 | "type": "FleetVehicleStatus", 4 | "battery": 0.81, 5 | "bearing": 80, 6 | "currentOperative": { 7 | "givenName": "John Smith", 8 | "jobTitle": "Ambulance Operator" 9 | }, 10 | "currentStatus": "finished", 11 | "dataProvider": "https://provider.example.com", 12 | "fleetVehicle": "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", 13 | "fleetVehicleOperation": "urn:ngsi-ld:FleetVehicleOperation:a4f0a07a-5aa6-11e8-b70f-4b9d36e53d7b", 14 | "inRestrictedArea": true, 15 | "lastFuellingAmount": 95, 16 | "lastKnownPosition": { 17 | "type": "Point", 18 | "coordinates": [ 19 | -104.99404, 20 | 39.75621 21 | ] 22 | }, 23 | "lastKnownPositionUpdatedAt": "2016-08-28T10:18:16Z", 24 | "mileageFromOdometer": 18756, 25 | "restFuelAmount": 28, 26 | "source": "https://source.example.com", 27 | "speed": 60, 28 | "unitCode": "KMH", 29 | "@context": [ 30 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 31 | ] 32 | } -------------------------------------------------------------------------------- /FleetVehicleStatus/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "battery_", "bearing_", "currentOperative__givenName_", "currentOperative__jobTitle_", "currentStatus_", "dataProvider_", "fleetVehicle_", "fleetVehicleOperation_", "inRestrictedArea_", "lastFuellingAmount_", "lastKnownPosition__type_", "lastKnownPosition__coordinates__0_", "lastKnownPosition__coordinates__1_", "lastKnownPositionUpdatedAt_", "mileageFromOdometer_", "restFuelAmount_", "source_", "speed_", "unitCode_", "@context__0_" 2 | "urn:ngsi-ld:FleetVehicleStatus:16ea1c5c-5aa6-11e8-8144-4b82063ca31c", "FleetVehicleStatus", "0.81", "80", "John Smith", "Ambulance Operator", "finished", "https://provider.example.com", "urn:ngsi-ld:FleetVehicle:84c6a3a8-5aa6-11e8-bedc-27e105edd16f", "urn:ngsi-ld:FleetVehicleOperation:a4f0a07a-5aa6-11e8-b70f-4b9d36e53d7b", "True", "95", "Point", "-104.99404", "39.75621", "2016-08-28T10:18:16Z", "18756", "28", "https://source.example.com", "60", "KMH", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /FleetVehicleStatus/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | This data model comes from the original project GSMA IoT project, https://www.gsma.com/iot/iot-big-data/. There are some minor adaptations to meet requirements of smart data models. 3 | notesMiddle: 4 | 5 | notesFooter: 6 | 7 | notesReadme: 8 | This data model comes from the original project GSMA IoT project, https://www.gsma.com/iot/iot-big-data/. -------------------------------------------------------------------------------- /FleetVehicleStatus/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model FleetVehicleStatus of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE currentStatus_type AS ENUM ('deployed','finished','servicing','starting','terminated');CREATE TYPE FleetVehicleStatus_type AS ENUM ('FleetVehicleStatus'); 3 | CREATE TABLE FleetVehicleStatus (address JSON, alternateName TEXT, areaServed TEXT, battery NUMERIC, bearing NUMERIC, currentOperative JSON, currentStatus currentStatus_type, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, id TEXT PRIMARY KEY, inRestrictedArea BOOLEAN, lastFuellingAmount NUMERIC, lastKnownPosition JSON, lastKnownPositionUpdatedAt TIMESTAMP, location JSON, mileageFromOdometer NUMERIC, name TEXT, owner JSON, restFuelAmount NUMERIC, seeAlso JSON, source TEXT, speed NUMERIC, type FleetVehicleStatus_type); -------------------------------------------------------------------------------- /ItemFlowObserved/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model ItemFlowObserved of the Subject []. 2 | currentAdopters: 3 | - 4 | adopter: Stephane ROUX 5 | description: Project Manager 6 | mail: stephane.Roux@nicecotedazur.org 7 | organization: Métropole Nice Côte d'Azur 8 | project: Data Lake 9 | comments: SmartCity Project 10 | startDate: January 2019 11 | -------------------------------------------------------------------------------- /ItemFlowObserved/code/README.md: -------------------------------------------------------------------------------- 1 | # ItemFlowObserved 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_ItemFlowObserved.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/ItemFlowObserved/code/code_for_using_dataModel.Transportation_ItemFlowObserved.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /ItemFlowObserved/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "FlowObserved:BFO-NCE-MNCA-SP-001", 3 | "type": "ItemFlowObserved", 4 | "address": { 5 | "addressCountry": "FR", 6 | "addressLocality": "Nice", 7 | "streetAddress": "Port Lympia" 8 | }, 9 | "areaServed": "Nice Harbor", 10 | "averageGapDistance": 35.28, 11 | "averageHeadwayTime": 156, 12 | "averageLength": 7.44, 13 | "averageSpeed": 2.7, 14 | "congested": false, 15 | "dateObserved": "2020-03-20T16:30:00Z", 16 | "dateObservedFrom": "2020-03-20T16:30:00Z", 17 | "dateObservedTo": "2020-03-20T22:30:00Z", 18 | "description": "Boat Flow Observed from Nice Harbor.", 19 | "itemSubType": "monoHull", 20 | "itemType": "yacht", 21 | "intensity": 12, 22 | "laneDirection": "outbound", 23 | "laneId": 1, 24 | "location": { 25 | "coordinates": [ 26 | 7.196545, 27 | 43.664809 28 | ], 29 | "type": "Point" 30 | }, 31 | "maxSpeed": 3.8, 32 | "minSpeed": 2.6, 33 | "name": "BFO-NCE-MNCA-SP-001", 34 | "occupancy": 0.1562, 35 | "refDevice": "Device:BFO-NCE-MNCA-SP-001-Dev-02", 36 | "reverseLane": false 37 | } -------------------------------------------------------------------------------- /ItemFlowObserved/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "address__addressCountry_", "address__addressLocality_", "address__streetAddress_", "areaServed_", "averageGapDistance_", "averageHeadwayTime_", "averageLength_", "averageSpeed_", "congested_", "dateObserved_", "dateObservedFrom_", "dateObservedTo_", "description_", "itemSubType_", "itemType_", "intensity_", "laneDirection_", "laneId_", "location__coordinates__0_", "location__coordinates__1_", "location__type_", "maxSpeed_", "minSpeed_", "name_", "occupancy_", "refDevice_", "reverseLane_" 2 | "FlowObserved:BFO-NCE-MNCA-SP-001", "ItemFlowObserved", "FR", "Nice", "Port Lympia", "Nice Harbor", "35.28", "156", "7.44", "2.7", "False", "2020-03-20T16:30:00Z", "2020-03-20T16:30:00Z", "2020-03-20T22:30:00Z", "Boat Flow Observed from Nice Harbor.", "monoHull", "yacht", "12", "outbound", "1", "7.196545", "43.664809", "Point", "3.8", "2.6", "BFO-NCE-MNCA-SP-001", "0.1562", "Device:BFO-NCE-MNCA-SP-001-Dev-02", "False" -------------------------------------------------------------------------------- /ItemFlowObserved/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "itemFlowObserved:BFO-NCE-MNCA-SP-001", 3 | "type": "ItemFlowObserved", 4 | "address": { 5 | "addressCountry": "FR", 6 | "addressLocality": "Nice", 7 | "streetAddress": "Port Lympia" 8 | }, 9 | "areaServed": "Nice Harbor", 10 | "averageGapDistance": 35.28, 11 | "averageHeadwayTime": 156, 12 | "averageLength": 7.44, 13 | "averageSpeed": 2.7, 14 | "congested": false, 15 | "dateObserved": "2020-03-20T16:30:00Z", 16 | "dateObservedFrom": "2020-03-20T16:30:00Z", 17 | "dateObservedTo": "2020-03-20T22:30:00Z", 18 | "description": "Boat Flow Observed from Nice Harbor.", 19 | "intensity": 12, 20 | "itemSubType": "monoHull", 21 | "itemType": "yacht", 22 | "laneDirection": "outbound", 23 | "laneId": 1, 24 | "location": { 25 | "coordinates": [ 26 | 7.196545, 27 | 43.664809 28 | ], 29 | "type": "Point" 30 | }, 31 | "maxSpeed": 3.8, 32 | "minSpeed": 2.6, 33 | "name": "BFO-NCE-MNCA-SP-001", 34 | "occupancy": 0.1562, 35 | "refDevice": "Device:BFO-NCE-MNCA-SP-001-Dev-02", 36 | "reverseLane": false, 37 | "@context": [ 38 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 39 | ] 40 | } 41 | -------------------------------------------------------------------------------- /ItemFlowObserved/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "address__addressCountry_", "address__addressLocality_", "address__streetAddress_", "areaServed_", "averageGapDistance_", "averageHeadwayTime_", "averageLength_", "averageSpeed_", "congested_", "dateObserved_", "dateObservedFrom_", "dateObservedTo_", "description_", "intensity_", "itemSubType_", "itemType_", "laneDirection_", "laneId_", "location__coordinates__0_", "location__coordinates__1_", "location__type_", "maxSpeed_", "minSpeed_", "name_", "occupancy_", "refDevice_", "reverseLane_", "@context__0_" 2 | "itemFlowObserved:BFO-NCE-MNCA-SP-001", "ItemFlowObserved", "FR", "Nice", "Port Lympia", "Nice Harbor", "35.28", "156", "7.44", "2.7", "False", "2020-03-20T16:30:00Z", "2020-03-20T16:30:00Z", "2020-03-20T22:30:00Z", "Boat Flow Observed from Nice Harbor.", "12", "monoHull", "yacht", "outbound", "1", "7.196545", "43.664809", "Point", "3.8", "2.6", "BFO-NCE-MNCA-SP-001", "0.1562", "Device:BFO-NCE-MNCA-SP-001-Dev-02", "False", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /ItemFlowObserved/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /ItemFlowObserved/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model ItemFlowObserved of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE itemType_type AS ENUM ('people','ship','vehicle','yacht');CREATE TYPE laneDirection_type AS ENUM ('forward','backward','inbound','outbound','right','left');CREATE TYPE ItemFlowObserved_type AS ENUM ('ItemFlowObserved'); 3 | CREATE TABLE ItemFlowObserved (address JSON, alternateName TEXT, areaServed TEXT, averageGapDistance NUMERIC, averageHeadwayTime NUMERIC, averageLength NUMERIC, averageSpeed NUMERIC, congested BOOLEAN, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, dateObserved TIMESTAMP, dateObservedFrom TIMESTAMP, dateObservedTo TIMESTAMP, description TEXT, id TEXT PRIMARY KEY, intensity NUMERIC, itemSubType TEXT, itemType itemType_type, laneDirection laneDirection_type, laneId NUMERIC, location JSON, maxSpeed NUMERIC, minSpeed NUMERIC, name TEXT, occupancy NUMERIC, owner JSON, reverseLane BOOLEAN, seeAlso JSON, source TEXT, type ItemFlowObserved_type); -------------------------------------------------------------------------------- /RestrictedTrafficArea/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model RestrictedTrafficArea of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /RestrictedTrafficArea/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/RestrictedTrafficArea/SmartDataModelBadge.png -------------------------------------------------------------------------------- /RestrictedTrafficArea/code/README.md: -------------------------------------------------------------------------------- 1 | # RestrictedTrafficArea 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_RestrictedTrafficArea.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/RestrictedTrafficArea/code/code_for_using_dataModel.Transportation_RestrictedTrafficArea.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example-geojsonfeature.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", 3 | "type": "Feature", 4 | "geometry": { 5 | "type": "geo:json", 6 | "value": { 7 | "type": "Point", 8 | "coordinates": [ 9 | 9.214544, 10 | 45.483353 11 | ] 12 | } 13 | }, 14 | "properties": { 15 | "type": "RestrictedTrafficArea", 16 | "category": { 17 | "type": "array", 18 | "value": [ 19 | "onlyPedestrian" 20 | ] 21 | }, 22 | "description": { 23 | "type": "string", 24 | "value": "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:" 25 | }, 26 | "location": { 27 | "type": "geo:json", 28 | "value": { 29 | "type": "Point", 30 | "coordinates": [ 31 | 9.214544, 32 | 45.483353 33 | ] 34 | } 35 | }, 36 | "name": { 37 | "type": "string", 38 | "value": "Corso Concordia Area" 39 | }, 40 | "notAllowedVehicleType": { 41 | "type": "array", 42 | "value": [ 43 | "anyVehicle" 44 | ] 45 | }, 46 | "regulation": { 47 | "type": "string", 48 | "value": "Decree:54785/2004, Deliberation:425/2004" 49 | }, 50 | "source": { 51 | "type": "string", 52 | "value": "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_" 53 | }, 54 | "validityEndDate": { 55 | "type": "DateTime", 56 | "value": "2049-12-31T23:00:00.00Z" 57 | }, 58 | "@context": [ 59 | "https://smartdatamodels.org/context.jsonld" 60 | ] 61 | } 62 | } -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example-geojsonfeature.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "geometry__type_", "geometry__value__type_", "geometry__value__coordinates__0_", "geometry__value__coordinates__1_", "properties__type_", "properties__category__type_", "properties__category__value__0_", "properties__description__type_", "properties__description__value_", "properties__location__type_", "properties__location__value__type_", "properties__location__value__coordinates__0_", "properties__location__value__coordinates__1_", "properties__name__type_", "properties__name__value_", "properties__notAllowedVehicleType__type_", "properties__notAllowedVehicleType__value__0_", "properties__regulation__type_", "properties__regulation__value_", "properties__source__type_", "properties__source__value_", "properties__validityEndDate__type_", "properties__validityEndDate__value_", "properties__@context__0_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", "Feature", "geo:json", "Point", "9.214544", "45.483353", "RestrictedTrafficArea", "array", "onlyPedestrian", "string", "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:", "geo:json", "Point", "9.214544", "45.483353", "string", "Corso Concordia Area", "array", "anyVehicle", "string", "Decree:54785/2004, Deliberation:425/2004", "string", "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_", "DateTime", "2049-12-31T23:00:00.00Z", "https://smartdatamodels.org/context.jsonld" -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", 3 | "type": "RestrictedTrafficArea", 4 | "category": { 5 | "type": "StructuredValue", 6 | "value": [ 7 | "onlyPedestrian" 8 | ] 9 | }, 10 | "description": { 11 | "type": "Text", 12 | "value": "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:" 13 | }, 14 | "location": { 15 | "type": "geo:json", 16 | "value": { 17 | "type": "Point", 18 | "coordinates": [ 19 | 9.214544, 20 | 45.483353 21 | ] 22 | } 23 | }, 24 | "name": { 25 | "type": "Text", 26 | "value": "Corso Concordia Area" 27 | }, 28 | "notAllowedVehicleType": { 29 | "type": "StructuredValue", 30 | "value": [ 31 | "anyVehicle" 32 | ] 33 | }, 34 | "regulation": { 35 | "type": "Text", 36 | "value": "Decree:54785/2004, Deliberation:425/2004" 37 | }, 38 | "source": { 39 | "type": "Text", 40 | "value": "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_" 41 | }, 42 | "validityEndDate": { 43 | "type": "DateTime", 44 | "value": "2049-12-31T23:00:00.00Z" 45 | } 46 | } -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "category__type_", "category__value__0_", "description__type_", "description__value_", "location__type_", "location__value__type_", "location__value__coordinates__0_", "location__value__coordinates__1_", "name__type_", "name__value_", "notAllowedVehicleType__type_", "notAllowedVehicleType__value__0_", "regulation__type_", "regulation__value_", "source__type_", "source__value_", "validityEndDate__type_", "validityEndDate__value_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", "RestrictedTrafficArea", "StructuredValue", "onlyPedestrian", "Text", "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:", "geo:json", "Point", "9.214544", "45.483353", "Text", "Corso Concordia Area", "StructuredValue", "anyVehicle", "Text", "Decree:54785/2004, Deliberation:425/2004", "Text", "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_", "DateTime", "2049-12-31T23:00:00.00Z" -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", 3 | "type": "RestrictedTrafficArea", 4 | "category": { 5 | "type": "Property", 6 | "value": [ 7 | "onlyPedestrian" 8 | ] 9 | }, 10 | "description": { 11 | "type": "Property", 12 | "value": "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:" 13 | }, 14 | "location": { 15 | "type": "GeoProperty", 16 | "value": { 17 | "type": "Point", 18 | "coordinates": [ 19 | 9.214544, 20 | 45.483353 21 | ] 22 | } 23 | }, 24 | "name": { 25 | "type": "Property", 26 | "value": "Corso Concordia Area" 27 | }, 28 | "notAllowedVehicleType": { 29 | "type": "Property", 30 | "value": [ 31 | "anyVehicle" 32 | ] 33 | }, 34 | "regulation": { 35 | "type": "Property", 36 | "value": "Decree:54785/2004, Deliberation:425/2004" 37 | }, 38 | "source": { 39 | "type": "Property", 40 | "value": "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_" 41 | }, 42 | "validityEndDate": { 43 | "type": "Property", 44 | "value": "2049-12-31T23:00:00.00Z" 45 | }, 46 | "@context": [ 47 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 48 | ] 49 | } 50 | -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "category__type_", "category__value__0_", "description__type_", "description__value_", "location__type_", "location__value__type_", "location__value__coordinates__0_", "location__value__coordinates__1_", "name__type_", "name__value_", "notAllowedVehicleType__type_", "notAllowedVehicleType__value__0_", "regulation__type_", "regulation__value_", "source__type_", "source__value_", "validityEndDate__type_", "validityEndDate__value_", "@context__0_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", "RestrictedTrafficArea", "Property", "onlyPedestrian", "Property", "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:", "GeoProperty", "Point", "9.214544", "45.483353", "Property", "Corso Concordia Area", "Property", "anyVehicle", "Property", "Decree:54785/2004, Deliberation:425/2004", "Property", "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_", "Property", "2049-12-31T23:00:00.00Z", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", 3 | "type": "RestrictedTrafficArea", 4 | "category": [ 5 | "onlyPedestrian" 6 | ], 7 | "description": "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:", 8 | "location": { 9 | "type": "Point", 10 | "coordinates": [ 11 | 9.214544, 12 | 45.483353 13 | ] 14 | }, 15 | "name": "Corso Concordia Area", 16 | "notAllowedVehicleType": [ 17 | "anyVehicle" 18 | ], 19 | "regulation": "Decree:54785/2004, Deliberation:425/2004", 20 | "source": "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_", 21 | "validityEndDate": "2049-12-31T23:00:00.00Z" 22 | } -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "category__0_", "description_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "name_", "notAllowedVehicleType__0_", "regulation_", "source_", "validityEndDate_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", "RestrictedTrafficArea", "onlyPedestrian", "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:", "Point", "9.214544", "45.483353", "Corso Concordia Area", "anyVehicle", "Decree:54785/2004, Deliberation:425/2004", "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_", "2049-12-31T23:00:00.00Z" -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", 3 | "type": "RestrictedTrafficArea", 4 | "category": [ 5 | "onlyPedestrian" 6 | ], 7 | "description": "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:", 8 | "location": { 9 | "type": "Point", 10 | "coordinates": [ 11 | 9.214544, 12 | 45.483353 13 | ] 14 | }, 15 | "name": "Corso Concordia Area", 16 | "notAllowedVehicleType": [ 17 | "anyVehicle" 18 | ], 19 | "regulation": "Decree:54785/2004, Deliberation:425/2004", 20 | "source": "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_", 21 | "validityEndDate": "2049-12-31T23:00:00.00Z", 22 | "@context": [ 23 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 24 | ] 25 | } -------------------------------------------------------------------------------- /RestrictedTrafficArea/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "category__0_", "description_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "name_", "notAllowedVehicleType__0_", "regulation_", "source_", "validityEndDate_", "@context__0_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", "RestrictedTrafficArea", "onlyPedestrian", "Panel:AP - Stretches:lato civici dispari da piazza Tricolore a via Kramer - Bollards: - Notes:", "Point", "9.214544", "45.483353", "Corso Concordia Area", "anyVehicle", "Decree:54785/2004, Deliberation:425/2004", "https://dati.comune.milano.it/dataset/ds51_trafficotrasporti_aree_pedonali_ztl_zone_30_", "2049-12-31T23:00:00.00Z", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /RestrictedTrafficArea/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | Data model coming from synchronicity project 3 | 4 | notesMiddle: 5 | 6 | notesFooter: 7 | -------------------------------------------------------------------------------- /RestrictedTrafficArea/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model RestrictedTrafficArea of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE RestrictedTrafficArea_type AS ENUM ('RestrictedTrafficArea'); 3 | CREATE TABLE RestrictedTrafficArea (address JSON, alternateName TEXT, areaServed TEXT, category JSON, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, id TEXT PRIMARY KEY, location JSON, name TEXT, notAllowedVehicleType JSON, owner JSON, regulation TEXT, restrictionExceptions JSON, restrictionValidityHours TEXT, security JSON, seeAlso JSON, source TEXT, specialRestrictions JSON, type RestrictedTrafficArea_type, validityEndDate TIMESTAMP, validityStartDate TIMESTAMP); -------------------------------------------------------------------------------- /RestrictionException/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model RestrictionException of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /RestrictionException/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/RestrictionException/SmartDataModelBadge.png -------------------------------------------------------------------------------- /RestrictionException/code/README.md: -------------------------------------------------------------------------------- 1 | # RestrictionException 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_RestrictionException.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/RestrictionException/code/code_for_using_dataModel.Transportation_RestrictionException.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /RestrictionException/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictionException:GeoJson:ds51-1", 3 | "type": "RestrictionException", 4 | "name": { 5 | "type": "Text", 6 | "value": "Corso Concordia Area" 7 | }, 8 | "allowedVehicleType": { 9 | "type": "StructuredValue", 10 | "value": [ 11 | "dieselCarEuro6", 12 | "petrolCarEuro6" 13 | ] 14 | }, 15 | "exceptionValidityHours": { 16 | "type": "Text", 17 | "value": "Tu,Th 16:00-20:00" 18 | }, 19 | "refVehicleModel": { 20 | "type": "StructuredValue", 21 | "value": [ 22 | "vehicle:VehicleModel:modelName-1" 23 | ] 24 | }, 25 | "refRestrictedTrafficArea": { 26 | "type": "Text", 27 | "value": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1" 28 | } 29 | } -------------------------------------------------------------------------------- /RestrictionException/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name__type_", "name__value_", "allowedVehicleType__type_", "allowedVehicleType__value__0_", "allowedVehicleType__value__1_", "exceptionValidityHours__type_", "exceptionValidityHours__value_", "refVehicleModel__type_", "refVehicleModel__value__0_", "refRestrictedTrafficArea__type_", "refRestrictedTrafficArea__value_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictionException:GeoJson:ds51-1", "RestrictionException", "Text", "Corso Concordia Area", "StructuredValue", "dieselCarEuro6", "petrolCarEuro6", "Text", "Tu,Th 16:00-20:00", "StructuredValue", "vehicle:VehicleModel:modelName-1", "Text", "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1" -------------------------------------------------------------------------------- /RestrictionException/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictionException:GeoJson:ds51-1", 3 | "type": "RestrictionException", 4 | "name": { 5 | "type": "string", 6 | "value": "Corso Concordia Area" 7 | }, 8 | "allowedVehicleType": { 9 | "type": "array", 10 | "value": [ 11 | "dieselCarEuro6", 12 | "petrolCarEuro6" 13 | ] 14 | }, 15 | "exceptionValidityHours": { 16 | "type": "string", 17 | "value": "Tu,Th 16:00-20:00" 18 | }, 19 | "refVehicleModel": { 20 | "type": "array", 21 | "value": [ 22 | "vehicle:VehicleModel:modelName-1" 23 | ] 24 | }, 25 | "refRestrictedTrafficArea": { 26 | "type": "string", 27 | "value": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1" 28 | }, 29 | "@context": [ 30 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 31 | ] 32 | } -------------------------------------------------------------------------------- /RestrictionException/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name__type_", "name__value_", "allowedVehicleType__type_", "allowedVehicleType__value__0_", "allowedVehicleType__value__1_", "exceptionValidityHours__type_", "exceptionValidityHours__value_", "refVehicleModel__type_", "refVehicleModel__value__0_", "refRestrictedTrafficArea__type_", "refRestrictedTrafficArea__value_", "@context__0_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictionException:GeoJson:ds51-1", "RestrictionException", "string", "Corso Concordia Area", "array", "dieselCarEuro6", "petrolCarEuro6", "string", "Tu,Th 16:00-20:00", "array", "vehicle:VehicleModel:modelName-1", "string", "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /RestrictionException/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictionException:GeoJson:ds51-1", 3 | "type": "RestrictionException", 4 | "name": "Corso Concordia Area", 5 | "allowedVehicleType": [ 6 | "dieselCarEuro6", 7 | "petrolCarEuro6" 8 | ], 9 | "exceptionValidityHours": "Tu,Th 16:00-20:00", 10 | "refVehicleModel": [ 11 | "vehicle:VehicleModel:modelName-1" 12 | ], 13 | "refRestrictedTrafficArea": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1" 14 | } -------------------------------------------------------------------------------- /RestrictionException/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name_", "allowedVehicleType__0_", "allowedVehicleType__1_", "exceptionValidityHours_", "refVehicleModel__0_", "refRestrictedTrafficArea_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictionException:GeoJson:ds51-1", "RestrictionException", "Corso Concordia Area", "dieselCarEuro6", "petrolCarEuro6", "Tu,Th 16:00-20:00", "vehicle:VehicleModel:modelName-1", "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1" -------------------------------------------------------------------------------- /RestrictionException/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictionException:GeoJson:ds51-1", 3 | "type": "RestrictionException", 4 | "allowedVehicleType": [ 5 | "dieselCarEuro6", 6 | "petrolCarEuro6" 7 | ], 8 | "exceptionValidityHours": "Tu,Th 16:00-20:00", 9 | "name": "Corso Concordia Area", 10 | "refRestrictedTrafficArea": "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", 11 | "refVehicleModel": [ 12 | "vehicle:VehicleModel:modelName-1" 13 | ], 14 | "@context": [ 15 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 16 | ] 17 | } -------------------------------------------------------------------------------- /RestrictionException/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "allowedVehicleType__0_", "allowedVehicleType__1_", "exceptionValidityHours_", "name_", "refRestrictedTrafficArea_", "refVehicleModel__0_", "@context__0_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictionException:GeoJson:ds51-1", "RestrictionException", "dieselCarEuro6", "petrolCarEuro6", "Tu,Th 16:00-20:00", "Corso Concordia Area", "urn:ngsi-ld:RestrictedTrafficArea:Milan:RestrictedTrafficAreas:GeoJson:ds51-1", "vehicle:VehicleModel:modelName-1", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /RestrictionException/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | Data model coming from synchronicity project 3 | 4 | notesMiddle: 5 | 6 | notesFooter: 7 | -------------------------------------------------------------------------------- /RestrictionException/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model RestrictionException of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE RestrictionException_type AS ENUM ('RestrictionException'); 3 | CREATE TABLE RestrictionException (address JSON, allowedVehicleType JSON, alternateName TEXT, areaServed TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, exceptionValidityHours TEXT, id TEXT PRIMARY KEY, location JSON, name TEXT, owner JSON, refVehicleModel JSON, seeAlso JSON, source TEXT, type RestrictionException_type); -------------------------------------------------------------------------------- /Road/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model Road of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /Road/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/Road/SmartDataModelBadge.png -------------------------------------------------------------------------------- /Road/code/README.md: -------------------------------------------------------------------------------- 1 | # Road 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_Road.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/Road/code/code_for_using_dataModel.Transportation_Road.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /Road/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "Spain-Road-A62", 3 | "type": "Road", 4 | "refRoadSegment": { 5 | "type": "StructuredValue", 6 | "value": [ 7 | "Spain-RoadSegment-A62-0-355-forwards", 8 | "Spain-RoadSegment-A62-0-355-backwards" 9 | ] 10 | }, 11 | "roadClass": { 12 | "type": "Text", 13 | "value": "motorway" 14 | }, 15 | "description": { 16 | "type": "Text", 17 | "value": "Autov\u00eda de Castilla" 18 | }, 19 | "responsible": { 20 | "type": "Text", 21 | "value": "Ministerio de Fomento - Gobierno de Espa\u00f1a" 22 | }, 23 | "length": { 24 | "type": "Number", 25 | "value": 355 26 | }, 27 | "alternateName": { 28 | "type": "Text", 29 | "value": "E-80" 30 | }, 31 | "name": { 32 | "type": "Text", 33 | "value": "A-62" 34 | } 35 | } -------------------------------------------------------------------------------- /Road/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "refRoadSegment__type_", "refRoadSegment__value__0_", "refRoadSegment__value__1_", "roadClass__type_", "roadClass__value_", "description__type_", "description__value_", "responsible__type_", "responsible__value_", "length__type_", "length__value_", "alternateName__type_", "alternateName__value_", "name__type_", "name__value_" 2 | "Spain-Road-A62", "Road", "StructuredValue", "Spain-RoadSegment-A62-0-355-forwards", "Spain-RoadSegment-A62-0-355-backwards", "Text", "motorway", "Text", "Autovía de Castilla", "Text", "Ministerio de Fomento - Gobierno de España", "Number", "355", "Text", "E-80", "Text", "A-62" -------------------------------------------------------------------------------- /Road/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:Road:Spain-Road-A62", 3 | "type": "Road", 4 | "alternateName": { 5 | "type": "Property", 6 | "value": "E-80" 7 | }, 8 | "description": { 9 | "type": "Property", 10 | "value": "Autov\u00eda de Castilla" 11 | }, 12 | "length": { 13 | "type": "Property", 14 | "value": 355 15 | }, 16 | "name": { 17 | "type": "Property", 18 | "value": "A-62" 19 | }, 20 | "refRoadSegment": { 21 | "type": "Relationship", 22 | "object": [ 23 | "urn:ngsi-ld:RoadSegment:Spain-RoadSegment-A62-0-355-forwards", 24 | "urn:ngsi-ld:RoadSegment:Spain-RoadSegment-A62-0-355-backwards" 25 | ] 26 | }, 27 | "responsible": { 28 | "type": "Property", 29 | "value": "Ministerio de Fomento - Gobierno de Espa\u00f1a" 30 | }, 31 | "roadClass": { 32 | "type": "Property", 33 | "value": "motorway" 34 | }, 35 | "@context": [ 36 | "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld", 37 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 38 | ] 39 | } 40 | -------------------------------------------------------------------------------- /Road/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "alternateName__type_", "alternateName__value_", "description__type_", "description__value_", "length__type_", "length__value_", "name__type_", "name__value_", "refRoadSegment__type_", "refRoadSegment__object__0_", "refRoadSegment__object__1_", "responsible__type_", "responsible__value_", "roadClass__type_", "roadClass__value_", "@context__0_", "@context__1_" 2 | "urn:ngsi-ld:Road:Spain-Road-A62", "Road", "Property", "E-80", "Property", "Autovía de Castilla", "Property", "355", "Property", "A-62", "Relationship", "urn:ngsi-ld:RoadSegment:Spain-RoadSegment-A62-0-355-forwards", "urn:ngsi-ld:RoadSegment:Spain-RoadSegment-A62-0-355-backwards", "Property", "Ministerio de Fomento - Gobierno de España", "Property", "motorway", "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /Road/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "Spain-Road-A62", 3 | "type": "Road", 4 | "name": "A-62", 5 | "alternateName": "E-80", 6 | "description": "Autov\u00eda de Castilla", 7 | "roadClass": "motorway", 8 | "length": 355, 9 | "refRoadSegment": [ 10 | "Spain-RoadSegment-A62-0-355-forwards", 11 | "Spain-RoadSegment-A62-0-355-backwards" 12 | ], 13 | "responsible": "Ministerio de Fomento - Gobierno de Espa\u00f1a" 14 | } -------------------------------------------------------------------------------- /Road/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name_", "alternateName_", "description_", "roadClass_", "length_", "refRoadSegment__0_", "refRoadSegment__1_", "responsible_" 2 | "Spain-Road-A62", "Road", "A-62", "E-80", "Autovía de Castilla", "motorway", "355", "Spain-RoadSegment-A62-0-355-forwards", "Spain-RoadSegment-A62-0-355-backwards", "Ministerio de Fomento - Gobierno de España" -------------------------------------------------------------------------------- /Road/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:Road:Spain-Road-A62", 3 | "type": "Road", 4 | "alternateName": "E-80", 5 | "description": "Autov\u00eda de Castilla", 6 | "length": 355, 7 | "name": "A-62", 8 | "refRoadSegment": [ 9 | "urn:ngsi-ld:RoadSegment:Spain-RoadSegment-A62-0-355-forwards", 10 | "urn:ngsi-ld:RoadSegment:Spain-RoadSegment-A62-0-355-backwards" 11 | ], 12 | "responsible": "Ministerio de Fomento - Gobierno de Espa\u00f1a", 13 | "roadClass": "motorway", 14 | "@context": [ 15 | "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld", 16 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 17 | ] 18 | } -------------------------------------------------------------------------------- /Road/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "alternateName_", "description_", "length_", "name_", "refRoadSegment__0_", "refRoadSegment__1_", "responsible_", "roadClass_", "@context__0_", "@context__1_" 2 | "urn:ngsi-ld:Road:Spain-Road-A62", "Road", "E-80", "Autovía de Castilla", "355", "A-62", "urn:ngsi-ld:RoadSegment:Spain-RoadSegment-A62-0-355-forwards", "urn:ngsi-ld:RoadSegment:Spain-RoadSegment-A62-0-355-backwards", "Ministerio de Fomento - Gobierno de España", "motorway", "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /Road/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | Roads are made up of one or more RoadSegment entities. Road segments are usually used to model the different carriageways of highways, for instance. The presence of dedicated bicycle lanes should be modelled using road segments as well. Road segments also play an important role when modelling roads with heterogeneous segments, for instance segments on which speed limits are different. This entity is primarily associated with the Automotive and Smart City vertical segments and related IoT applications. This data model has been developed in cooperation with mobile operators and the GSMA. 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /Road/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model Road of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE roadClass_type AS ENUM ('motorway','primary','residential','secondary','service','tertiary','trunk','unclassified');CREATE TYPE Road_type AS ENUM ('Road'); 3 | CREATE TABLE Road (address JSON, alternateName TEXT, annotations JSON, areaServed TEXT, color TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, id TEXT PRIMARY KEY, image TEXT, length NUMERIC, location JSON, name TEXT, owner JSON, refRoadSegment JSON, responsible TEXT, roadClass roadClass_type, seeAlso JSON, source TEXT, type Road_type); -------------------------------------------------------------------------------- /Road/swagger.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | # Copyleft (c) 2022 Contributors to Smart Data Models initiative 3 | # 4 | 5 | 6 | components: 7 | schemas: 8 | Road: 9 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/Road/model.yaml#/Road" 10 | info: 11 | description: | 12 | This entity contains a harmonised geographic and contextual description of a road. 13 | title: Road 14 | version: "0.0.1" 15 | openapi: "3.0.0" 16 | 17 | paths: 18 | /ngsi-ld/v1/entities: 19 | get: 20 | description: "Retrieve a set of entities which matches a specific query from an NGSI-LD system" 21 | parameters: 22 | - 23 | in: query 24 | name: type 25 | required: true 26 | schema: 27 | enum: 28 | - Road 29 | type: string 30 | responses: 31 | ? "200" 32 | : 33 | content: 34 | application/ld+json: 35 | examples: 36 | keyvalues: 37 | summary: "Key-Values Pairs" 38 | value: 39 | - 40 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/Road/examples/example.json" 41 | normalized: 42 | summary: "Normalized NGSI-LD" 43 | value: 44 | - 45 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/Road/examples/example-normalized.jsonld" 46 | description: OK 47 | tags: 48 | - ngsi-ld 49 | tags: 50 | - 51 | description: "NGSI-LD Linked-data Format" 52 | name: ngsi-ld 53 | -------------------------------------------------------------------------------- /RoadAccident/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | 2 | description: This is a compilation list of the current adopters of the data model [Data model] of the Subject [Subject]. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 3 | currentAdopters: 4 | - 5 | adopter: 6 | description: 7 | mail: 8 | organization: 9 | project: 10 | comments: 11 | startDate: 12 | -------------------------------------------------------------------------------- /RoadAccident/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/RoadAccident/SmartDataModelBadge.png -------------------------------------------------------------------------------- /RoadAccident/code/README.md: -------------------------------------------------------------------------------- 1 | # RoadAccident 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_RoadAccident.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/RoadAccident/code/code_for_using_dataModel.Transportation_RoadAccident.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /RoadAccident/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | Data model coming from synchronicity project 3 | 4 | notesMiddle: 5 | 6 | notesFooter: 7 | -------------------------------------------------------------------------------- /RoadAccident/swagger.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | # Copyleft (c) 2022 Contributors to Smart Data Models initiative 3 | # 4 | 5 | 6 | components: 7 | schemas: 8 | RoadAccident: 9 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/RoadAccident/model.yaml#/RoadAccident" 10 | info: 11 | description: | 12 | A road accident description with causes and aftermath. First version developed in Synchronicity project 13 | title: RoadAccident 14 | version: "0.0.1" 15 | openapi: "3.0.0" 16 | 17 | paths: 18 | /ngsi-ld/v1/entities: 19 | get: 20 | description: "Retrieve a set of entities which matches a specific query from an NGSI-LD system" 21 | parameters: 22 | - 23 | in: query 24 | name: type 25 | required: true 26 | schema: 27 | enum: 28 | - RoadAccident 29 | type: string 30 | responses: 31 | ? "200" 32 | : 33 | content: 34 | application/ld+json: 35 | examples: 36 | keyvalues: 37 | summary: "Key-Values Pairs" 38 | value: 39 | - 40 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/RoadAccident/examples/example.json" 41 | normalized: 42 | summary: "Normalized NGSI-LD" 43 | value: 44 | - 45 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/RoadAccident/examples/example-normalized.jsonld" 46 | description: OK 47 | tags: 48 | - ngsi-ld 49 | tags: 50 | - 51 | description: "NGSI-LD Linked-data Format" 52 | name: ngsi-ld 53 | -------------------------------------------------------------------------------- /RoadSegment/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model RoadSegment of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: Gert Van de Wouwer 5 | description: Extra attributes status and statusDescription to allow modelling temporary road closures. 6 | mail: Gert.VandeWouwer@gmail.com 7 | organization: Digipolis Antwerpen 8 | project: 9 | comments: Can be used to model the effect of bridge or lock openings on traffic that runs over them. 10 | startDate: '2022-01-15' 11 | - 12 | adopter: IUDX 13 | description: A Data Model for road segments in the cities. 14 | mail: 15 | organization: 16 | project: 17 | comments: 18 | startDate: '2022-08-17' -------------------------------------------------------------------------------- /RoadSegment/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/RoadSegment/SmartDataModelBadge.png -------------------------------------------------------------------------------- /RoadSegment/code/README.md: -------------------------------------------------------------------------------- 1 | # RoadSegment 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_RoadSegment.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/RoadSegment/code/code_for_using_dataModel.Transportation_RoadSegment.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /RoadSegment/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | Road segments can include several lanes. This data model allows to convey road segments made up of heterogeneous lanes (different in their usage, speed, height, etc.). Lanes are identified by using integer numbers between 1 and n, being number 1 the lane to the right when going forwards. The forward direction is the direction denoted by the vector which goes from the segment"s start point to the segment"s end point. This is the same convention as the one used by OpenStreetMap. This entity is primarily associated with the Automotive and Smart City vertical segments and related IoT applications. This data model has been developed in cooperation with mobile operators and the GSMA. 3 | notesMiddle: 4 | The properties `laneUsage` and those which convey the maximum allowed parameters can be dynamic, for instance, a lane direction can be temporarily changed to improve traffic conditions. 5 | notesFooter: -------------------------------------------------------------------------------- /SpecialRestriction/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model SpecialRestriction of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /SpecialRestriction/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/SpecialRestriction/SmartDataModelBadge.png -------------------------------------------------------------------------------- /SpecialRestriction/code/README.md: -------------------------------------------------------------------------------- 1 | # SpecialRestriction 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_SpecialRestriction.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/SpecialRestriction/code/code_for_using_dataModel.Transportation_SpecialRestriction.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /SpecialRestriction/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:SpecialRestriction:GeoJson:ds51-1", 3 | "type": "SpecialRestriction", 4 | "name": { 5 | "type": "Text", 6 | "value": "Corso Concordia Area" 7 | }, 8 | "notAllowedVehicleType": { 9 | "type": "StructuredValue", 10 | "value": [ 11 | "dieselCarEuro0", 12 | "petrolCarEuro0" 13 | ] 14 | }, 15 | "refVehicleModel": { 16 | "type": "StructuredValue", 17 | "value": [ 18 | "vehicle:VehicleModel:modelName-1" 19 | ] 20 | }, 21 | "refRestrictedTrafficArea": { 22 | "type": "Text", 23 | "value": "urn:ngsi-ld:SpecialRestriction:RestrictedTrafficArea:Milan:GeoJson:ds51-1" 24 | }, 25 | "restrictionValidityHours": { 26 | "type": "Text", 27 | "value": "Tu,Th 16:00-20:00" 28 | } 29 | } -------------------------------------------------------------------------------- /SpecialRestriction/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name__type_", "name__value_", "notAllowedVehicleType__type_", "notAllowedVehicleType__value__0_", "notAllowedVehicleType__value__1_", "refVehicleModel__type_", "refVehicleModel__value__0_", "refRestrictedTrafficArea__type_", "refRestrictedTrafficArea__value_", "restrictionValidityHours__type_", "restrictionValidityHours__value_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:SpecialRestriction:GeoJson:ds51-1", "SpecialRestriction", "Text", "Corso Concordia Area", "StructuredValue", "dieselCarEuro0", "petrolCarEuro0", "StructuredValue", "vehicle:VehicleModel:modelName-1", "Text", "urn:ngsi-ld:SpecialRestriction:RestrictedTrafficArea:Milan:GeoJson:ds51-1", "Text", "Tu,Th 16:00-20:00" -------------------------------------------------------------------------------- /SpecialRestriction/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:SpecialRestriction:GeoJson:ds51-1", 3 | "type": "SpecialRestriction", 4 | "name": { 5 | "type": "string", 6 | "value": "Corso Concordia Area" 7 | }, 8 | "notAllowedVehicleType": { 9 | "type": "array", 10 | "value": [ 11 | "dieselCarEuro0", 12 | "petrolCarEuro0" 13 | ] 14 | }, 15 | "refVehicleModel": { 16 | "type": "array", 17 | "value": ["vehicle:VehicleModel:modelName-1"] 18 | }, 19 | "refRestrictedTrafficArea": { 20 | "type": "string", 21 | "value": "urn:ngsi-ld:SpecialRestriction:RestrictedTrafficArea:Milan:GeoJson:ds51-1" 22 | }, 23 | "restrictionValidityHours": { 24 | "type": "string", 25 | "value": "Tu,Th 16:00-20:00" 26 | }, 27 | "@context": [ 28 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 29 | ] 30 | } -------------------------------------------------------------------------------- /SpecialRestriction/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name__type_", "name__value_", "notAllowedVehicleType__type_", "notAllowedVehicleType__value__0_", "notAllowedVehicleType__value__1_", "refVehicleModel__type_", "refVehicleModel__value__0_", "refRestrictedTrafficArea__type_", "refRestrictedTrafficArea__value_", "restrictionValidityHours__type_", "restrictionValidityHours__value_", "@context__0_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:SpecialRestriction:GeoJson:ds51-1", "SpecialRestriction", "string", "Corso Concordia Area", "array", "dieselCarEuro0", "petrolCarEuro0", "array", "vehicle:VehicleModel:modelName-1", "string", "urn:ngsi-ld:SpecialRestriction:RestrictedTrafficArea:Milan:GeoJson:ds51-1", "string", "Tu,Th 16:00-20:00", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /SpecialRestriction/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:SpecialRestriction:GeoJson:ds51-1", 3 | "type": "SpecialRestriction", 4 | "name": "Corso Concordia Area", 5 | "notAllowedVehicleType": [ 6 | "dieselCarEuro0", 7 | "petrolCarEuro0" 8 | ], 9 | "restrictionValidityHours": "Tu,Th 16:00-20:00", 10 | "refVehicleModel": [ 11 | "vehicle:VehicleModel:modelName-1" 12 | ], 13 | "refRestrictedTrafficArea": "urn:ngsi-ld:SpecialRestriction:RestrictedTrafficArea:Milan:GeoJson:ds51-1" 14 | } -------------------------------------------------------------------------------- /SpecialRestriction/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name_", "notAllowedVehicleType__0_", "notAllowedVehicleType__1_", "restrictionValidityHours_", "refVehicleModel__0_", "refRestrictedTrafficArea_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:SpecialRestriction:GeoJson:ds51-1", "SpecialRestriction", "Corso Concordia Area", "dieselCarEuro0", "petrolCarEuro0", "Tu,Th 16:00-20:00", "vehicle:VehicleModel:modelName-1", "urn:ngsi-ld:SpecialRestriction:RestrictedTrafficArea:Milan:GeoJson:ds51-1" -------------------------------------------------------------------------------- /SpecialRestriction/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:RestrictedTrafficArea:Milan:SpecialRestriction:GeoJson:ds51-1", 3 | "type": "SpecialRestriction", 4 | "name": "Corso Concordia Area", 5 | "notAllowedVehicleType": [ 6 | "dieselCarEuro0", 7 | "petrolCarEuro0" 8 | ], 9 | "refRestrictedTrafficArea": "urn:ngsi-ld:SpecialRestriction:RestrictedTrafficArea:Milan:GeoJson:ds51-1", 10 | "refVehicleModel": [ 11 | "vehicle:VehicleModel:modelName-1" 12 | ], 13 | "restrictionValidityHours": "Tu,Th 16:00-20:00", 14 | "@context": [ 15 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 16 | ] 17 | } -------------------------------------------------------------------------------- /SpecialRestriction/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name_", "notAllowedVehicleType__0_", "notAllowedVehicleType__1_", "refRestrictedTrafficArea_", "refVehicleModel__0_", "restrictionValidityHours_", "@context__0_" 2 | "urn:ngsi-ld:RestrictedTrafficArea:Milan:SpecialRestriction:GeoJson:ds51-1", "SpecialRestriction", "Corso Concordia Area", "dieselCarEuro0", "petrolCarEuro0", "urn:ngsi-ld:SpecialRestriction:RestrictedTrafficArea:Milan:GeoJson:ds51-1", "vehicle:VehicleModel:modelName-1", "Tu,Th 16:00-20:00", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /SpecialRestriction/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | Data model coming from synchronicity project 3 | 4 | notesMiddle: 5 | 6 | notesFooter: 7 | -------------------------------------------------------------------------------- /SpecialRestriction/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model SpecialRestriction of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE SpecialRestriction_type AS ENUM ('SpecialRestriction'); 3 | CREATE TABLE SpecialRestriction (address JSON, alternateName TEXT, areaServed TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, id TEXT PRIMARY KEY, location JSON, name TEXT, notAllowedVehicleType JSON, owner JSON, refVehicleModel JSON, restrictionValidityHours TEXT, seeAlso JSON, source TEXT, type SpecialRestriction_type); -------------------------------------------------------------------------------- /TrafficFlowObserved/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model TrafficFlowObserved of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /TrafficFlowObserved/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/TrafficFlowObserved/SmartDataModelBadge.png -------------------------------------------------------------------------------- /TrafficFlowObserved/code/README.md: -------------------------------------------------------------------------------- 1 | # TrafficFlowObserved 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_TrafficFlowObserved.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/TrafficFlowObserved/code/code_for_using_dataModel.Transportation_TrafficFlowObserved.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /TrafficFlowObserved/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dateObserved__type_", "dateObserved__value_", "laneDirection__type_", "laneDirection__value_", "dateObservedFrom__type_", "dateObservedFrom__value_", "averageVehicleLength__type_", "averageVehicleLength__value_", "averageHeadwayTime__type_", "averageHeadwayTime__value_", "occupancy__type_", "occupancy__value_", "reversedLane__type_", "reversedLane__value_", "dateObservedTo__type_", "dateObservedTo__value_", "intensity__type_", "intensity__value_", "laneId__type_", "laneId__value_", "location__type_", "location__value__type_", "location__value__coordinates__0__0_", "location__value__coordinates__0__1_", "location__value__coordinates__1__0_", "location__value__coordinates__1__1_", "location__value__coordinates__2__0_", "location__value__coordinates__2__1_", "address__type_", "address__value__addressLocality_", "address__value__addressCountry_", "address__value__streetAddress_", "averageVehicleSpeed__type_", "averageVehicleSpeed__value_" 2 | "TrafficFlowObserved-Valladolid-osm-60821110", "TrafficFlowObserved", "DateTime", "2016-12-07T11:10:00/2016-12-07T11:15:00", "Text", "forward", "DateTime", "2016-12-07T11:10:00Z", "Number", "9.87", "Number", "0.5", "Number", "0.76", "Boolean", "False", "DateTime", "2016-12-07T11:15:00Z", "Number", "197", "Boolean", "True", "geo:json", "LineString", "-4.73735395519672", "41.6538181849672", "-4.73414858659993", "41.6600594193478", "-4.73447575302641", "41.659585195093", "StructuredValue", "Valladolid", "ES", "Avenida de Salamanca", "Number", "52.6" -------------------------------------------------------------------------------- /TrafficFlowObserved/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "TrafficFlowObserved-Valladolid-osm-60821110", 3 | "type": "TrafficFlowObserved", 4 | "laneId": 1, 5 | "address": { 6 | "streetAddress": "Avenida de Salamanca", 7 | "addressLocality": "Valladolid", 8 | "addressCountry": "ES" 9 | }, 10 | "location": { 11 | "type": "LineString", 12 | "coordinates": [ 13 | [ 14 | -4.73735395519672, 15 | 41.6538181849672 16 | ], 17 | [ 18 | -4.73414858659993, 19 | 41.6600594193478 20 | ], 21 | [ 22 | -4.73447575302641, 23 | 41.659585195093 24 | ] 25 | ] 26 | }, 27 | "dateObserved": "2016-12-07T11:10:00/2016-12-07T11:15:00", 28 | "dateObservedFrom": "2016-12-07T11:10:00Z", 29 | "dateObservedTo": "2016-12-07T11:15:00Z", 30 | "averageHeadwayTime": 0.5, 31 | "intensity": 197, 32 | "occupancy": 0.76, 33 | "averageVehicleSpeed": 52.6, 34 | "averageVehicleLength": 9.87, 35 | "reversedLane": false, 36 | "laneDirection": "forward" 37 | } -------------------------------------------------------------------------------- /TrafficFlowObserved/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "laneId_", "address__streetAddress_", "address__addressLocality_", "address__addressCountry_", "location__type_", "location__coordinates__0__0_", "location__coordinates__0__1_", "location__coordinates__1__0_", "location__coordinates__1__1_", "location__coordinates__2__0_", "location__coordinates__2__1_", "dateObserved_", "dateObservedFrom_", "dateObservedTo_", "averageHeadwayTime_", "intensity_", "occupancy_", "averageVehicleSpeed_", "averageVehicleLength_", "reversedLane_", "laneDirection_" 2 | "TrafficFlowObserved-Valladolid-osm-60821110", "TrafficFlowObserved", "1", "Avenida de Salamanca", "Valladolid", "ES", "LineString", "-4.73735395519672", "41.6538181849672", "-4.73414858659993", "41.6600594193478", "-4.73447575302641", "41.659585195093", "2016-12-07T11:10:00/2016-12-07T11:15:00", "2016-12-07T11:10:00Z", "2016-12-07T11:15:00Z", "0.5", "197", "0.76", "52.6", "9.87", "False", "forward" -------------------------------------------------------------------------------- /TrafficFlowObserved/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:TrafficFlowObserved:TrafficFlowObserved-Valladolid-osm-60821110", 3 | "type": "TrafficFlowObserved", 4 | "address": { 5 | "addressCountry": "ES", 6 | "addressLocality": "Valladolid", 7 | "streetAddress": "Avenida de Salamanca", 8 | "type": "PostalAddress" 9 | }, 10 | "averageHeadwayTime": 0.5, 11 | "averageVehicleLength": 9.87, 12 | "averageVehicleSpeed": 52.6, 13 | "dateObserved": "2016-12-07T11:10:00/2016-12-07T11:15:00", 14 | "dateObservedFrom": "2016-12-07T11:10:00Z", 15 | "dateObservedTo": "2016-12-07T11:15:00Z", 16 | "intensity": 197, 17 | "laneDirection": "forward", 18 | "laneId": 1, 19 | "location": { 20 | "coordinates": [ 21 | [ 22 | -4.73735395519672, 23 | 41.6538181849672 24 | ], 25 | [ 26 | -4.73414858659993, 27 | 41.6600594193478 28 | ], 29 | [ 30 | -4.73447575302641, 31 | 41.659585195093 32 | ] 33 | ], 34 | "type": "LineString" 35 | }, 36 | "occupancy": 0.76, 37 | "reversedLane": false, 38 | "@context": [ 39 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 40 | ] 41 | } -------------------------------------------------------------------------------- /TrafficFlowObserved/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "address__addressCountry_", "address__addressLocality_", "address__streetAddress_", "address__type_", "averageHeadwayTime_", "averageVehicleLength_", "averageVehicleSpeed_", "dateObserved_", "dateObservedFrom_", "dateObservedTo_", "intensity_", "laneDirection_", "laneId_", "location__coordinates__0__0_", "location__coordinates__0__1_", "location__coordinates__1__0_", "location__coordinates__1__1_", "location__coordinates__2__0_", "location__coordinates__2__1_", "location__type_", "occupancy_", "reversedLane_", "@context__0_" 2 | "urn:ngsi-ld:TrafficFlowObserved:TrafficFlowObserved-Valladolid-osm-60821110", "TrafficFlowObserved", "ES", "Valladolid", "Avenida de Salamanca", "PostalAddress", "0.5", "9.87", "52.6", "2016-12-07T11:10:00/2016-12-07T11:15:00", "2016-12-07T11:10:00Z", "2016-12-07T11:15:00Z", "197", "forward", "1", "-4.73735395519672", "41.6538181849672", "-4.73414858659993", "41.6600594193478", "-4.73447575302641", "41.659585195093", "LineString", "0.76", "False", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /TrafficFlowObserved/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | This entity is primarily associated with the Automotive and Smart City vertical segments and related IoT applications. 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /TrafficFlowObserved/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model TrafficFlowObserved of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE laneDirection_type AS ENUM ('forward','backward');CREATE TYPE TrafficFlowObserved_type AS ENUM ('TrafficFlowObserved');CREATE TYPE vehicleType_type AS ENUM ('agriculturalVehicle','bicycle','bus','minibus','car','caravan','tram','tanker','carWithCaravan','carWithTrailer','lorry','moped','motorcycle','motorcycleWithSideCar','motorscooter','trailer','van','constructionOrMaintenanceVehicle','trolley','binTrolley','sweepingMachine','cleaningTrolley'); 3 | CREATE TABLE TrafficFlowObserved (address JSON, alternateName TEXT, areaServed TEXT, averageGapDistance NUMERIC, averageHeadwayTime NUMERIC, averageVehicleLength NUMERIC, averageVehicleSpeed NUMERIC, congested BOOLEAN, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, dateObserved TEXT, dateObservedFrom TIMESTAMP, dateObservedTo TIMESTAMP, description TEXT, id TEXT PRIMARY KEY, intensity NUMERIC, laneDirection laneDirection_type, laneId NUMERIC, location JSON, name TEXT, occupancy NUMERIC, owner JSON, refRoadSegment TEXT, reversedLane BOOLEAN, seeAlso JSON, source TEXT, type TrafficFlowObserved_type, vehicleSubType TEXT, vehicleType vehicleType_type); -------------------------------------------------------------------------------- /TrafficViolation/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model TrafficViolation of the Subject datamodel.Trasnportation. 2 | currentAdopters: 3 | - 4 | adopter: IUDX 5 | description: Data Model for Traffic Violations registered and E-Challans generated in Cities. 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /TrafficViolation/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/TrafficViolation/SmartDataModelBadge.png -------------------------------------------------------------------------------- /TrafficViolation/code/README.md: -------------------------------------------------------------------------------- 1 | # TrafficViolation 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_TrafficViolation.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/TrafficViolation/code/code_for_using_dataModel.Transportation_TrafficViolation.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /TrafficViolation/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "ngsi-ld:Trafficviolation:234R:0212", 3 | "type": "TrafficViolation", 4 | "amountCollected": { 5 | "type": "Number", 6 | "value": 10500 7 | }, 8 | "mediaURL": { 9 | "type": "Text", 10 | "value": "https://www.google.com/" 11 | }, 12 | "equipmentId": { 13 | "type": "Text", 14 | "value": "4" 15 | }, 16 | "equipmentType": { 17 | "type": "Text", 18 | "value": "Camera" 19 | }, 20 | "titleCode": { 21 | "type": "Text", 22 | "value": "11" 23 | }, 24 | "reportId": { 25 | "type": "Text", 26 | "value": "182" 27 | }, 28 | "observationDateTime": { 29 | "type": "DateTime", 30 | "value": "2021-03-11T15:51:02+05:30" 31 | }, 32 | "paymentStatus": { 33 | "type": "Text", 34 | "value": "Paid" 35 | } 36 | } -------------------------------------------------------------------------------- /TrafficViolation/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "amountCollected__type_", "amountCollected__value_", "mediaURL__type_", "mediaURL__value_", "equipmentId__type_", "equipmentId__value_", "equipmentType__type_", "equipmentType__value_", "titleCode__type_", "titleCode__value_", "reportId__type_", "reportId__value_", "observationDateTime__type_", "observationDateTime__value_", "paymentStatus__type_", "paymentStatus__value_" 2 | "ngsi-ld:Trafficviolation:234R:0212", "TrafficViolation", "Number", "10500", "Text", "https://www.google.com/", "Text", "4", "Text", "Camera", "Text", "11", "Text", "182", "DateTime", "2021-03-11T15:51:02+05:30", "Text", "Paid" -------------------------------------------------------------------------------- /TrafficViolation/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "ngsi-ld:Trafficviolation:234R:0212", 3 | "type": "TrafficViolation", 4 | "amountCollected": { 5 | "type": "Property", 6 | "value": 10500 7 | }, 8 | "equipmentId": { 9 | "type": "Property", 10 | "value": "4" 11 | }, 12 | "equipmentType": { 13 | "type": "Property", 14 | "value": "Camera" 15 | }, 16 | "mediaURL": { 17 | "type": "Property", 18 | "value": "https://www.google.com/" 19 | }, 20 | "observationDateTime": { 21 | "type": "Property", 22 | "value": { 23 | "@type": "DateTime", 24 | "@value": "2021-03-11T15:51:02+05:30" 25 | } 26 | }, 27 | "paymentStatus": { 28 | "type": "Property", 29 | "value": "Paid" 30 | }, 31 | "reportId": { 32 | "type": "Property", 33 | "value": "182" 34 | }, 35 | "titleCode": { 36 | "type": "Property", 37 | "value": "11" 38 | }, 39 | "@context": [ 40 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 41 | ] 42 | } -------------------------------------------------------------------------------- /TrafficViolation/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "amountCollected__type_", "amountCollected__value_", "equipmentId__type_", "equipmentId__value_", "equipmentType__type_", "equipmentType__value_", "mediaURL__type_", "mediaURL__value_", "observationDateTime__type_", "observationDateTime__value__@type_", "observationDateTime__value__@value_", "paymentStatus__type_", "paymentStatus__value_", "reportId__type_", "reportId__value_", "titleCode__type_", "titleCode__value_", "@context__0_" 2 | "ngsi-ld:Trafficviolation:234R:0212", "TrafficViolation", "Property", "10500", "Property", "4", "Property", "Camera", "Property", "https://www.google.com/", "Property", "DateTime", "2021-03-11T15:51:02+05:30", "Property", "Paid", "Property", "182", "Property", "11", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /TrafficViolation/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "ngsi-ld:Trafficviolation:234R:0212", 3 | "type": "TrafficViolation", 4 | "amountCollected": 10500, 5 | "mediaURL": "https://www.google.com/", 6 | "equipmentId": "4", 7 | "equipmentType": "Camera", 8 | "titleCode": "11", 9 | "reportId": "182", 10 | "observationDateTime": "2021-03-11T15:51:02+05:30", 11 | "paymentStatus": "Paid" 12 | } -------------------------------------------------------------------------------- /TrafficViolation/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "amountCollected_", "mediaURL_", "equipmentId_", "equipmentType_", "titleCode_", "reportId_", "observationDateTime_", "paymentStatus_" 2 | "ngsi-ld:Trafficviolation:234R:0212", "TrafficViolation", "10500", "https://www.google.com/", "4", "Camera", "11", "182", "2021-03-11T15:51:02+05:30", "Paid" -------------------------------------------------------------------------------- /TrafficViolation/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "ngsi-ld:Trafficviolation:234R:0212", 3 | "type": "TrafficViolation", 4 | "amountCollected": 10500, 5 | "equipmentId": "4", 6 | "equipmentType": "Camera", 7 | "mediaURL": "https://www.google.com/", 8 | "observationDateTime": "2021-03-11T15:51:02+05:30", 9 | "paymentStatus": "Paid", 10 | "reportId": "182", 11 | "titleCode": "11", 12 | "@context": [ 13 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 14 | ] 15 | } -------------------------------------------------------------------------------- /TrafficViolation/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "amountCollected_", "equipmentId_", "equipmentType_", "mediaURL_", "observationDateTime_", "paymentStatus_", "reportId_", "titleCode_", "@context__0_" 2 | "ngsi-ld:Trafficviolation:234R:0212", "TrafficViolation", "10500", "4", "Camera", "https://www.google.com/", "2021-03-11T15:51:02+05:30", "Paid", "182", "11", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /TrafficViolation/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /TrafficViolation/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model TrafficViolation of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE paymentStatus_type AS ENUM ('Paid','Unpaid');CREATE TYPE TrafficViolation_type AS ENUM ('TrafficViolation'); 3 | CREATE TABLE TrafficViolation (address JSON, alternateName TEXT, amountCollected NUMERIC, areaServed TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, equipmentId TEXT, equipmentType TEXT, id TEXT PRIMARY KEY, location JSON, mediaURL TEXT, name TEXT, observationDateTime TIMESTAMP, owner JSON, paymentStatus paymentStatus_type, reportId TEXT, seeAlso JSON, source TEXT, titleCode TEXT, type TrafficViolation_type); -------------------------------------------------------------------------------- /TrafficViolation/swagger.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | # Copyleft (c) 2022 Contributors to Smart Data Models initiative 3 | # 4 | 5 | 6 | components: 7 | schemas: 8 | TrafficViolation: 9 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/TrafficViolation/model.yaml#/TrafficViolation" 10 | info: 11 | description: | 12 | A Data Model for Traffic Violations registered and E-Challans generated in Cities. 13 | title: TrafficViolation 14 | version: "0.0.1" 15 | openapi: "3.0.0" 16 | 17 | paths: 18 | /ngsi-ld/v1/entities: 19 | get: 20 | description: "Retrieve a set of entities which matches a specific query from an NGSI-LD system" 21 | parameters: 22 | - 23 | in: query 24 | name: type 25 | required: true 26 | schema: 27 | enum: 28 | - TrafficViolation 29 | type: string 30 | responses: 31 | ? "200" 32 | : 33 | content: 34 | application/ld+json: 35 | examples: 36 | keyvalues: 37 | summary: "Key-Values Pairs" 38 | value: 39 | - 40 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/TrafficViolation/examples/example.json" 41 | normalized: 42 | summary: "Normalized NGSI-LD" 43 | value: 44 | - 45 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/TrafficViolation/examples/example-normalized.jsonld" 46 | description: OK 47 | tags: 48 | - ngsi-ld 49 | tags: 50 | - 51 | description: "NGSI-LD Linked-data Format" 52 | name: ngsi-ld 53 | -------------------------------------------------------------------------------- /TransportStation/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model TransportStation of the Subject Transportation. 2 | currentAdopters: 3 | - 4 | adopter: Stephane ROUX 5 | description: Project Manager 6 | mail: stephane.Roux@nicecotedazur.org 7 | organization: Métropole Nice Côte d'Azur 8 | project: Data Lake 9 | comments: SmartCity Project 10 | startDate: January 2019 11 | -------------------------------------------------------------------------------- /TransportStation/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/TransportStation/SmartDataModelBadge.png -------------------------------------------------------------------------------- /TransportStation/code/README.md: -------------------------------------------------------------------------------- 1 | # TransportStation 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_TransportStation.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/TransportStation/code/code_for_using_dataModel.Transportation_TransportStation.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /TransportStation/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | 3 | notesMiddle: 4 | 5 | nootesFooter: -------------------------------------------------------------------------------- /TransportStation/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model TransportStation of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE installationMode_type AS ENUM ('aerial','ground','underGround','underSea');CREATE TYPE locationType_type AS ENUM ('0','1','2','3','4');CREATE TYPE TransportStation_type AS ENUM ('TransportStation');CREATE TYPE wheelChairAccessible_type AS ENUM ('0','1','2'); 3 | CREATE TABLE TransportStation (address JSON, alternateName TEXT, architect TEXT, areaServed TEXT, commissioningDate TIMESTAMP, constructionDate TIMESTAMP, contactPoint JSON, contractingAuthority TEXT, contractingCompany TEXT, currencyAccepted JSON, dataProvider TEXT, dateCreated TIMESTAMP, dateLastReported TIMESTAMP, dateModified TIMESTAMP, description TEXT, dimension JSON, featuredArtist JSON, id TEXT PRIMARY KEY, installationMode installationMode_type, inventory JSON, levelId NUMERIC, location JSON, locationType locationType_type, name TEXT, openingHoursSpecification JSON, owner JSON, paymentAccepted JSON, platformCode NUMERIC, seeAlso JSON, services JSON, source TEXT, stationConnected JSON, stationType JSON, type TransportStation_type, webSite TEXT, wheelChairAccessible wheelChairAccessible_type, zoneId TEXT); -------------------------------------------------------------------------------- /Vehicle/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model Vehicle of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: Road closure information for the Antwerp fire brigade. 5 | description: By inspecting the status and statusDescription attributes, one can determine if a particular roadSegment is available for regular traffic. Due to events like bridges or locks that open for ships, the road may be temporarely closed and traffic is forced to wait or take a detour. 6 | mail: gert.vandewouwer@digipolis.be 7 | organization: Digipolis Antwerpen 8 | project: 9 | comments: The added attributes may also be used for other events that temporarely limit of block traffic on a roadSegment. 10 | startDate: 2022-01-15 11 | - 12 | adopter: ADDIX Internet Services GmbH 13 | description: https://addix.net/leistungen/smart-city.html 14 | mail: vertrieb@addix.net 15 | organization: Smart City 16 | project: CAPTN 5G 17 | comments: Extension of the enumeration for the attribute vehicleType to also cover different watercraft. 18 | startDate: 2022-11-03 19 | - 20 | adopter: SEDIMARK project 21 | description: Urban bike mobility planning use case in Santander 22 | mail: 23 | organization: 24 | project: https://sedimark.eu/ 25 | comments: 26 | startDate: 27 | -------------------------------------------------------------------------------- /Vehicle/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/Vehicle/SmartDataModelBadge.png -------------------------------------------------------------------------------- /Vehicle/code/README.md: -------------------------------------------------------------------------------- 1 | # Vehicle 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_Vehicle.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/Vehicle/code/code_for_using_dataModel.Transportation_Vehicle.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /Vehicle/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "vehicle:WasteManagement:1", 3 | "type": "Vehicle", 4 | "vehicleType": "lorry", 5 | "battery": 0.81, 6 | "category": [ 7 | "municipalServices" 8 | ], 9 | "location": { 10 | "type": "Point", 11 | "coordinates": [ 12 | -3.164485591715449, 13 | 40.62785133667262 14 | ] 15 | }, 16 | "name": "C Recogida 1", 17 | "speed": 50, 18 | "cargoWeight": 314, 19 | "serviceStatus": "onRoute", 20 | "serviceProvided": [ 21 | "garbageCollection", 22 | "wasteContainerCleaning" 23 | ], 24 | "areaServed": "Centro", 25 | "refVehicleModel": "vehiclemodel:econic", 26 | "vehiclePlateIdentifier": "3456ABC", 27 | "bearing": 43, 28 | "fuelEfficiency": 13, 29 | "fuelType": "Petrol", 30 | "fuelFilled": 6, 31 | "tripNetWeightCollected": 12, 32 | "vehicleTrackerDevice": "Installed", 33 | "wardId": "4", 34 | "license_plate": "KA052134", 35 | "currentTripCount": 1, 36 | "reportId": "21645", 37 | "zoneName": "South Zone", 38 | "vehicleAltitude": "600", 39 | "deviceSimNumber": "9942142573", 40 | "wardName": "Kempegowda Ward", 41 | "deviceBatteryStatus": "connected", 42 | "ignitionStatus": true, 43 | "vehicleRunningStatus": "running", 44 | "observationDateTime": "2021-03-11T15:51:02+05:30", 45 | "serviceOnDuty": false, 46 | "emergencyVehicleType": "ambulance", 47 | "municipalityInfo": { 48 | "district": "Bangalore Urban", 49 | "ulbName": "BMC", 50 | "cityId": "23", 51 | "wardId": "23", 52 | "stateName": "Karnataka", 53 | "cityName": "Bangalore", 54 | "zoneName": "South", 55 | "wardName": "Bangalore Urban", 56 | "zoneId": "2", 57 | "wardNum": 4 58 | } 59 | } -------------------------------------------------------------------------------- /Vehicle/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "vehicleType_", "battery_", "category__0_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "name_", "speed_", "cargoWeight_", "serviceStatus_", "serviceProvided__0_", "serviceProvided__1_", "areaServed_", "refVehicleModel_", "vehiclePlateIdentifier_", "bearing_", "fuelEfficiency_", "fuelType_", "fuelFilled_", "tripNetWeightCollected_", "vehicleTrackerDevice_", "wardId_", "license_plate_", "currentTripCount_", "reportId_", "zoneName_", "vehicleAltitude_", "deviceSimNumber_", "wardName_", "deviceBatteryStatus_", "ignitionStatus_", "vehicleRunningStatus_", "observationDateTime_", "serviceOnDuty_", "emergencyVehicleType_", "municipalityInfo__district_", "municipalityInfo__ulbName_", "municipalityInfo__cityId_", "municipalityInfo__wardId_", "municipalityInfo__stateName_", "municipalityInfo__cityName_", "municipalityInfo__zoneName_", "municipalityInfo__wardName_", "municipalityInfo__zoneId_", "municipalityInfo__wardNum_" 2 | "vehicle:WasteManagement:1", "Vehicle", "lorry", "0.81", "municipalServices", "Point", "-3.164485591715449", "40.62785133667262", "C Recogida 1", "50", "314", "onRoute", "garbageCollection", "wasteContainerCleaning", "Centro", "vehiclemodel:econic", "3456ABC", "43", "13", "Petrol", "6", "12", "Installed", "4", "KA052134", "1", "21645", "South Zone", "600", "9942142573", "Kempegowda Ward", "connected", "True", "running", "2021-03-11T15:51:02+05:30", "False", "ambulance", "Bangalore Urban", "BMC", "23", "23", "Karnataka", "Bangalore", "South", "Bangalore Urban", "2", "4" -------------------------------------------------------------------------------- /Vehicle/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "areaServed_", "battery_", "bearing_", "cargoWeight_", "category__0_", "currentTripCount_", "deviceBatteryStatus_", "deviceSimNumber_", "emergencyVehicleType_", "fuelEfficiency_", "fuelFilled_", "fuelType_", "ignitionStatus_", "license_plate_", "location__coordinates__0_", "location__coordinates__1_", "location__type_", "municipalityInfo__district_", "municipalityInfo__ulbName_", "municipalityInfo__cityId_", "municipalityInfo__wardId_", "municipalityInfo__stateName_", "municipalityInfo__cityName_", "municipalityInfo__zoneName_", "municipalityInfo__wardName_", "municipalityInfo__zoneId_", "municipalityInfo__wardNum_", "name_", "observationDateTime_", "refVehicleModel_", "reportId_", "serviceOnDuty_", "serviceProvided__0_", "serviceProvided__1_", "serviceStatus_", "speed_", "tripNetWeightCollected_", "vehicleAltitude_", "vehiclePlateIdentifier_", "vehicleRunningStatus_", "vehicleTrackerDevice_", "vehicleType_", "wardId_", "wardName_", "zoneName_", "@context__0_" 2 | "urn:ngsi-ld:Vehicle:vehicle:WasteManagement:1", "Vehicle", "Centro", "0.81", "43", "314", "municipalServices", "1", "connected", "9942142573", "ambulance", "13", "6", "Petrol", "True", "KA052134", "-3.164485591715449", "40.62785133667262", "Point", "Bangalore Urban", "BMC", "23", "23", "Karnataka", "Bangalore", "South", "Bangalore Urban", "2", "4", "C Recogida 1", "2021-03-11T15:51:02+05:30", "urn:ngsi-ld:VehicleModel:vehiclemodel:econic", "21645", "False", "garbageCollection", "wasteContainerCleaning", "onRoute", "50", "12", "600", "3456ABC", "running", "Installed", "lorry", "4", "Kempegowda Ward", "South Zone", "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" -------------------------------------------------------------------------------- /Vehicle/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /Vehicle/swagger.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | # Copyleft (c) 2022 Contributors to Smart Data Models initiative 3 | # 4 | 5 | 6 | components: 7 | schemas: 8 | Vehicle: 9 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/Vehicle/model.yaml#/Vehicle" 10 | info: 11 | description: | 12 | This entity models a particular vehicle model, including all properties which are common to multiple vehicle instances belonging to such model. 13 | title: Vehicle 14 | version: "0.2.2" 15 | openapi: "3.0.0" 16 | 17 | paths: 18 | /ngsi-ld/v1/entities: 19 | get: 20 | description: "Retrieve a set of entities which matches a specific query from an NGSI-LD system" 21 | parameters: 22 | - 23 | in: query 24 | name: type 25 | required: true 26 | schema: 27 | enum: 28 | - Vehicle 29 | type: string 30 | responses: 31 | ? "200" 32 | : 33 | content: 34 | application/ld+json: 35 | examples: 36 | keyvalues: 37 | summary: "Key-Values Pairs" 38 | value: 39 | - 40 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/Vehicle/examples/example.json" 41 | normalized: 42 | summary: "Normalized NGSI-LD" 43 | value: 44 | - 45 | $ref: "https://smart-data-models.github.io/dataModel.Transportation/Vehicle/examples/example-normalized.jsonld" 46 | description: OK 47 | tags: 48 | - ngsi-ld 49 | tags: 50 | - 51 | description: "NGSI-LD Linked-data Format" 52 | name: ngsi-ld 53 | -------------------------------------------------------------------------------- /VehicleFault/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model Vehicle Fault of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /VehicleFault/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/VehicleFault/SmartDataModelBadge.png -------------------------------------------------------------------------------- /VehicleFault/code/README.md: -------------------------------------------------------------------------------- 1 | # VehicleFault 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_VehicleFault.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/VehicleFault/code/code_for_using_dataModel.Transportation_VehicleFault.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /VehicleFault/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:VehicleFault:4939200a-5ef5-4266-8c91-1f82ad3b543b", 3 | "type": "VehicleFault", 4 | "source": { 5 | "type": "Text", 6 | "value": "https://source.example.com" 7 | }, 8 | "dataProvider": { 9 | "type": "Text", 10 | "value": "https://provider.example.com" 11 | }, 12 | "vehicle": { 13 | "type": "Text", 14 | "value": "urn:ngsi-ld:Vehicle:1fa179a6-b507-4857-ad72-eb5513ef05c6" 15 | }, 16 | "observedAt": { 17 | "type": "DateTime", 18 | "value": "2017-05-04T10:18:16Z" 19 | }, 20 | "eventType": { 21 | "type": "Text", 22 | "value": "emergency" 23 | }, 24 | "location": { 25 | "type": "geo:json", 26 | "value": { 27 | "type": "Point", 28 | "coordinates": [ 29 | -104.99404, 30 | 39.75621 31 | ] 32 | } 33 | }, 34 | "processingType": { 35 | "type": "Text", 36 | "value": "systemHandled" 37 | }, 38 | "resolvedAt": { 39 | "type": "DateTime", 40 | "value": "2017-05-04T10:18:16Z" 41 | }, 42 | "dtCode": { 43 | "type": "Text", 44 | "value": "EMERG-1234-a" 45 | }, 46 | "faultLog": { 47 | "type": "Text", 48 | "value": "Emergency stop. Fault with engine" 49 | } 50 | } -------------------------------------------------------------------------------- /VehicleFault/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "source__type_", "source__value_", "dataProvider__type_", "dataProvider__value_", "vehicle__type_", "vehicle__value_", "observedAt__type_", "observedAt__value_", "eventType__type_", "eventType__value_", "location__type_", "location__value__type_", "location__value__coordinates__0_", "location__value__coordinates__1_", "processingType__type_", "processingType__value_", "resolvedAt__type_", "resolvedAt__value_", "dtCode__type_", "dtCode__value_", "faultLog__type_", "faultLog__value_" 2 | "urn:ngsi-ld:VehicleFault:4939200a-5ef5-4266-8c91-1f82ad3b543b", "VehicleFault", "Text", "https://source.example.com", "Text", "https://provider.example.com", "Text", "urn:ngsi-ld:Vehicle:1fa179a6-b507-4857-ad72-eb5513ef05c6", "DateTime", "2017-05-04T10:18:16Z", "Text", "emergency", "geo:json", "Point", "-104.99404", "39.75621", "Text", "systemHandled", "DateTime", "2017-05-04T10:18:16Z", "Text", "EMERG-1234-a", "Text", "Emergency stop. Fault with engine" -------------------------------------------------------------------------------- /VehicleFault/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:VehicleFault:4939200a-5ef5-4266-8c91-1f82ad3b543b", 3 | "type": "VehicleFault", 4 | "dataProvider": { 5 | "type": "Property", 6 | "value": "https://provider.example.com" 7 | }, 8 | "dtCode": { 9 | "type": "Property", 10 | "value": "EMERG-1234-a" 11 | }, 12 | "eventType": { 13 | "type": "Property", 14 | "value": "emergency" 15 | }, 16 | "faultLog": { 17 | "type": "Property", 18 | "value": "Emergency stop. Fault with engine" 19 | }, 20 | "location": { 21 | "type": "GeoProperty", 22 | "value": { 23 | "type": "Point", 24 | "coordinates": [ 25 | -104.99404, 26 | 39.75621 27 | ] 28 | } 29 | }, 30 | "observedAt": { 31 | "type": "Property", 32 | "value": { 33 | "@type": "DateTime", 34 | "@value": "2017-05-04T10:18:16Z" 35 | } 36 | }, 37 | "processingType": { 38 | "type": "Property", 39 | "value": "systemHandled" 40 | }, 41 | "resolvedAt": { 42 | "type": "Property", 43 | "value": { 44 | "@type": "DateTime", 45 | "@value": "2017-05-04T10:18:16Z" 46 | } 47 | }, 48 | "source": { 49 | "type": "Property", 50 | "value": "https://source.example.com" 51 | }, 52 | "vehicle": { 53 | "type": "Relationship", 54 | "object": "urn:ngsi-ld:Vehicle:1fa179a6-b507-4857-ad72-eb5513ef05c6" 55 | }, 56 | "@context": [ 57 | "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" 58 | ] 59 | } 60 | -------------------------------------------------------------------------------- /VehicleFault/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dataProvider__type_", "dataProvider__value_", "dtCode__type_", "dtCode__value_", "eventType__type_", "eventType__value_", "faultLog__type_", "faultLog__value_", "location__type_", "location__value__type_", "location__value__coordinates__0_", "location__value__coordinates__1_", "observedAt__type_", "observedAt__value__@type_", "observedAt__value__@value_", "processingType__type_", "processingType__value_", "resolvedAt__type_", "resolvedAt__value__@type_", "resolvedAt__value__@value_", "source__type_", "source__value_", "vehicle__type_", "vehicle__object_", "@context__0_" 2 | "urn:ngsi-ld:VehicleFault:4939200a-5ef5-4266-8c91-1f82ad3b543b", "VehicleFault", "Property", "https://provider.example.com", "Property", "EMERG-1234-a", "Property", "emergency", "Property", "Emergency stop. Fault with engine", "GeoProperty", "Point", "-104.99404", "39.75621", "Property", "DateTime", "2017-05-04T10:18:16Z", "Property", "systemHandled", "Property", "DateTime", "2017-05-04T10:18:16Z", "Property", "https://source.example.com", "Relationship", "urn:ngsi-ld:Vehicle:1fa179a6-b507-4857-ad72-eb5513ef05c6", "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" -------------------------------------------------------------------------------- /VehicleFault/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:VehicleFault:4939200a-5ef5-4266-8c91-1f82ad3b543b", 3 | "type": "VehicleFault", 4 | "dateCreated": "2017-01-01T01:20:00Z", 5 | "dateModified": "2017-05-04T12:30:00Z", 6 | "source": "https://source.example.com", 7 | "dataProvider": "https://provider.example.com", 8 | "vehicle": "urn:ngsi-ld:Vehicle:1fa179a6-b507-4857-ad72-eb5513ef05c6", 9 | "observedAt": "2017-05-04T10:18:16Z", 10 | "eventType": "emergency", 11 | "location": { 12 | "type": "Point", 13 | "coordinates": [ 14 | -104.99404, 15 | 39.75621 16 | ] 17 | }, 18 | "processingType": "systemHandled", 19 | "resolvedAt": "2017-05-04T10:18:16Z", 20 | "dtCode": "EMERG-1234-a", 21 | "faultLog": "Emergency stop. Fault with engine" 22 | } 23 | -------------------------------------------------------------------------------- /VehicleFault/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dateCreated_", "dateModified_", "source_", "dataProvider_", "vehicle_", "observedAt_", "eventType_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "processingType_", "resolvedAt_", "dtCode_", "faultLog_" 2 | "urn:ngsi-ld:VehicleFault:4939200a-5ef5-4266-8c91-1f82ad3b543b", "VehicleFault", "2017-01-01T01:20:00Z", "2017-05-04T12:30:00Z", "https://source.example.com", "https://provider.example.com", "urn:ngsi-ld:Vehicle:1fa179a6-b507-4857-ad72-eb5513ef05c6", "2017-05-04T10:18:16Z", "emergency", "Point", "-104.99404", "39.75621", "systemHandled", "2017-05-04T10:18:16Z", "EMERG-1234-a", "Emergency stop. Fault with engine" -------------------------------------------------------------------------------- /VehicleFault/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:VehicleFault:4939200a-5ef5-4266-8c91-1f82ad3b543b", 3 | "type": "VehicleFault", 4 | "dataProvider": "https://provider.example.com", 5 | "dtCode": "EMERG-1234-a", 6 | "eventType": "emergency", 7 | "faultLog": "Emergency stop. Fault with engine", 8 | "location": { 9 | "type": "Point", 10 | "coordinates": [ 11 | -104.99404, 12 | 39.75621 13 | ] 14 | }, 15 | "observedAt": "2017-05-04T10:18:16Z", 16 | "processingType": "systemHandled", 17 | "resolvedAt": "2017-05-04T10:18:16Z", 18 | "source": "https://source.example.com", 19 | "vehicle": "urn:ngsi-ld:Vehicle:1fa179a6-b507-4857-ad72-eb5513ef05c6", 20 | "@context": [ 21 | "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" 22 | ] 23 | } 24 | -------------------------------------------------------------------------------- /VehicleFault/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "dataProvider_", "dtCode_", "eventType_", "faultLog_", "location__type_", "location__coordinates__0_", "location__coordinates__1_", "observedAt_", "processingType_", "resolvedAt_", "source_", "vehicle_", "@context__0_" 2 | "urn:ngsi-ld:VehicleFault:4939200a-5ef5-4266-8c91-1f82ad3b543b", "VehicleFault", "https://provider.example.com", "EMERG-1234-a", "emergency", "Emergency stop. Fault with engine", "Point", "-104.99404", "39.75621", "2017-05-04T10:18:16Z", "systemHandled", "2017-05-04T10:18:16Z", "https://source.example.com", "urn:ngsi-ld:Vehicle:1fa179a6-b507-4857-ad72-eb5513ef05c6", "https://smart-data-models.github.io/dataModel.Transportation/context.jsonld" -------------------------------------------------------------------------------- /VehicleFault/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /VehicleFault/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model VehicleFault of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE eventType_type AS ENUM ('collision','emergency','harshAccel','harshDecel','auxBatteryWarn','milWarn');CREATE TYPE VehicleFault_type AS ENUM ('VehicleFault'); 3 | CREATE TABLE VehicleFault (address JSON, alternateName TEXT, areaServed TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, description TEXT, dtCode TEXT, eventType eventType_type, faultLog TEXT, id TEXT PRIMARY KEY, location JSON, model TEXT, name TEXT, observedAt TIMESTAMP, owner JSON, processingType TEXT, resolvedAt TIMESTAMP, seeAlso JSON, source TEXT, type VehicleFault_type, vehicleType TEXT); -------------------------------------------------------------------------------- /VehicleModel/ADOPTERS.yaml: -------------------------------------------------------------------------------- 1 | description: This is a compilation list of the current adopters of the data model VehicleModel of the Subject dataModel.Transportation. All fields are non mandatory. More info at https://smart-data-models.github.io/data-models/templates/dataModel/CURRENT_ADOPTERS.yaml 2 | currentAdopters: 3 | - 4 | adopter: 5 | description: 6 | mail: 7 | organization: 8 | project: 9 | comments: 10 | startDate: 11 | -------------------------------------------------------------------------------- /VehicleModel/SmartDataModelBadge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/ed857c5d9c2e8dbb1c85208e22bbbc7c563017a7/VehicleModel/SmartDataModelBadge.png -------------------------------------------------------------------------------- /VehicleModel/code/README.md: -------------------------------------------------------------------------------- 1 | # VehicleModel 2 | 3 | ### List of code samples 4 | 5 | 6 | 7 | 8 | [code_for_using_dataModel.Transportation_VehicleModel.py](https://github.com/smart-data-models/dataModel.Transportation/blob/master/VehicleModel/code/code_for_using_dataModel.Transportation_VehicleModel.py) 9 | 10 | 11 | 12 | 13 | ### Contribution 14 | You can raise an [issue](https://github.com/smart-data-models/dataModel.Transportation/issues) or submit your [PR](https://github.com/smart-data-models/dataModel.Transportation/pulls) on existing data models 15 | -------------------------------------------------------------------------------- /VehicleModel/examples/example-normalized.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "vehiclemodel:econic", 3 | "type": "VehicleModel", 4 | "name": { 5 | "type": "Text", 6 | "value": "MBenz-Econic2014" 7 | }, 8 | "cargoVolume": { 9 | "type": "Number", 10 | "value": 1000 11 | }, 12 | "modelName": { 13 | "type": "Text", 14 | "value": "Econic" 15 | }, 16 | "brandName": { 17 | "type": "Text", 18 | "value": "Mercedes Benz" 19 | }, 20 | "manufacturerName": { 21 | "type": "Text", 22 | "value": "Daimler" 23 | }, 24 | "fuelType": { 25 | "type": "Text", 26 | "value": "diesel" 27 | }, 28 | "vehicleType": { 29 | "type": "Text", 30 | "value": "lorry" 31 | } 32 | } -------------------------------------------------------------------------------- /VehicleModel/examples/example-normalized.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name__type_", "name__value_", "cargoVolume__type_", "cargoVolume__value_", "modelName__type_", "modelName__value_", "brandName__type_", "brandName__value_", "manufacturerName__type_", "manufacturerName__value_", "fuelType__type_", "fuelType__value_", "vehicleType__type_", "vehicleType__value_" 2 | "vehiclemodel:econic", "VehicleModel", "Text", "MBenz-Econic2014", "Number", "1000", "Text", "Econic", "Text", "Mercedes Benz", "Text", "Daimler", "Text", "diesel", "Text", "lorry" -------------------------------------------------------------------------------- /VehicleModel/examples/example-normalized.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:VehicleModel:vehiclemodel:econic", 3 | "type": "VehicleModel", 4 | "brandName": { 5 | "type": "Property", 6 | "value": "Mercedes Benz" 7 | }, 8 | "cargoVolume": { 9 | "type": "Property", 10 | "value": 1000 11 | }, 12 | "fuelType": { 13 | "type": "Property", 14 | "value": "diesel" 15 | }, 16 | "manufacturerName": { 17 | "type": "Property", 18 | "value": "Daimler" 19 | }, 20 | "modelName": { 21 | "type": "Property", 22 | "value": "Econic" 23 | }, 24 | "name": { 25 | "type": "Property", 26 | "value": "MBenz-Econic2014" 27 | }, 28 | "vehicleType": { 29 | "type": "Property", 30 | "value": "lorry" 31 | }, 32 | "@context": [ 33 | "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld", 34 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 35 | ] 36 | } 37 | -------------------------------------------------------------------------------- /VehicleModel/examples/example-normalized.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "brandName__type_", "brandName__value_", "cargoVolume__type_", "cargoVolume__value_", "fuelType__type_", "fuelType__value_", "manufacturerName__type_", "manufacturerName__value_", "modelName__type_", "modelName__value_", "name__type_", "name__value_", "vehicleType__type_", "vehicleType__value_", "@context__0_", "@context__1_" 2 | "urn:ngsi-ld:VehicleModel:vehiclemodel:econic", "VehicleModel", "Property", "Mercedes Benz", "Property", "1000", "Property", "diesel", "Property", "Daimler", "Property", "Econic", "Property", "MBenz-Econic2014", "Property", "lorry", "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /VehicleModel/examples/example.json: -------------------------------------------------------------------------------- 1 | { 2 | "id": "vehiclemodel:econic", 3 | "type": "VehicleModel", 4 | "name": "MBenz-Econic2014", 5 | "brandName": "Mercedes Benz", 6 | "modelName": "Econic", 7 | "manufacturerName": "Daimler", 8 | "vehicleType": "lorry", 9 | "cargoVolume": 1000, 10 | "fuelType": "diesel" 11 | } -------------------------------------------------------------------------------- /VehicleModel/examples/example.json.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "name_", "brandName_", "modelName_", "manufacturerName_", "vehicleType_", "cargoVolume_", "fuelType_" 2 | "vehiclemodel:econic", "VehicleModel", "MBenz-Econic2014", "Mercedes Benz", "Econic", "Daimler", "lorry", "1000", "diesel" -------------------------------------------------------------------------------- /VehicleModel/examples/example.jsonld: -------------------------------------------------------------------------------- 1 | { 2 | "id": "urn:ngsi-ld:VehicleModel:vehiclemodel:econic", 3 | "type": "VehicleModel", 4 | "brandName": "Mercedes Benz", 5 | "cargoVolume": 1000, 6 | "fuelType": "diesel", 7 | "manufacturerName": "Daimler", 8 | "modelName": "Econic", 9 | "name": "MBenz-Econic2014", 10 | "vehicleType": "lorry", 11 | "@context": [ 12 | "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld", 13 | "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" 14 | ] 15 | } -------------------------------------------------------------------------------- /VehicleModel/examples/example.jsonld.csv: -------------------------------------------------------------------------------- 1 | "id_", "type_", "brandName_", "cargoVolume_", "fuelType_", "manufacturerName_", "modelName_", "name_", "vehicleType_", "@context__0_", "@context__1_" 2 | "urn:ngsi-ld:VehicleModel:vehiclemodel:econic", "VehicleModel", "Mercedes Benz", "1000", "diesel", "Daimler", "Econic", "MBenz-Econic2014", "lorry", "https://uri.etsi.org/ngsi-ld/v1/ngsi-ld-core-context.jsonld", "https://raw.githubusercontent.com/smart-data-models/dataModel.Transportation/master/context.jsonld" -------------------------------------------------------------------------------- /VehicleModel/notes.yaml: -------------------------------------------------------------------------------- 1 | notesHeader: 2 | 3 | notesMiddle: 4 | 5 | notesFooter: -------------------------------------------------------------------------------- /VehicleModel/schema.sql: -------------------------------------------------------------------------------- 1 | /* (Beta) Export of data model VehicleModel of the subject dataModel.Transportation for a PostgreSQL database. Pending translation of enumerations and multityped attributes */ 2 | CREATE TYPE fuelType_type AS ENUM ('autogas','biodiesel','cng','diesel','electric','ethanol','gasoline','hybrid_electric_diesel','hybrid_electric_petrol','hydrogen','lpg','petrol','petrol(unleaded)','petrol(leaded)','other');CREATE TYPE VehicleModel_type AS ENUM ('VehicleModel');CREATE TYPE vehicleType_type AS ENUM ('agriculturalVehicle','bicycle','binTrolley','bus','car','caravan','carWithCaravan','carWithTrailer','cleaningTrolley','constructionOrMaintenanceVehicle','lorry','minibus','moped','motorcycle','motorcycleWithSideCar','motorscooter','sweepingMachine','tanker','trailer','tram','van','trolley'); 3 | CREATE TABLE VehicleModel (address JSON, alternateName TEXT, annotations JSON, areaServed TEXT, brandName TEXT, cargoVolume NUMERIC, color TEXT, dataProvider TEXT, dateCreated TIMESTAMP, dateModified TIMESTAMP, depth NUMERIC, description TEXT, fuelConsumption NUMERIC, fuelType fuelType_type, height NUMERIC, id TEXT PRIMARY KEY, image TEXT, location JSON, manufacturerName TEXT, modelName TEXT, name TEXT, owner JSON, seeAlso JSON, source TEXT, type VehicleModel_type, url TEXT, vehicleEngine TEXT, vehicleModelDate TIMESTAMP, vehicleType vehicleType_type, weight NUMERIC, width NUMERIC); -------------------------------------------------------------------------------- /notes.yaml: -------------------------------------------------------------------------------- 1 | notesPrevious: 2 | These data models describe the main entities involved with smart applications 3 | that deal with transportation issues. This set of entities is primarily 4 | associated with the Automotive and Smart City vertical segments and related IoT 5 | applications. 6 | 7 | When feasible, references to existing schema.org entity types and attributes are 8 | included. 9 | 10 | These models have been devised to be as generic as possible, thus allowing to 11 | deal with different scenarios 12 | 13 | - Traffic flow monitoring 14 | - Private Vehicles. 15 | - Public Vehicles (Buses, Trains, etc.). 16 | - Municipal Vehicles (pick up lorries, cleaning units, ...) 17 | - Special Vehicles (ambulances, fire brigades, ...) 18 | 19 | notesEnd: 20 | "" --------------------------------------------------------------------------------