├── DataLoop.png ├── Data_Sample.PNG └── README.md /DataLoop.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhiyongc/Seattle-Loop-Data/567b79cd922f5943984a05649c8014fc764e42fe/DataLoop.png -------------------------------------------------------------------------------- /Data_Sample.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhiyongc/Seattle-Loop-Data/567b79cd922f5943984a05649c8014fc764e42fe/Data_Sample.PNG -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Seattle Inductive Loop Detector Dataset V.1 (2015) 2 | 3 | > Dataset for *Network-wide Traffic Forecasting* 4 | 5 | > 6 | 7 | #### The data is collected by the inductive loop detectors deployed on freeways in Seattle area. The freeways contains I-5, I-405, I-90, and SR-520, shown in the above picture. This dataset contains spatio-temporal speed information of the freeway system. In the picture, each blue icon demonstrates loop detectors at a milepost. The speed information at a milepost is averaged from multiple loop detectors on the mainlanes in a same direction at the specific milepost. The time interval of the dataset is 5-minute. 8 | --- 9 | #### The data download link contains a list of files: 10 | * `speed_matrix_2015`: Loop Speed Matrix, which is a pickled file that can be read by pandas or other python packages. 11 | * `Loop_Seattle_2015_A.npy`: Loop Adjacency Matrix, which is a numpy matrix to describe the traffic network structure as a graph. 12 | * `Loop_Seattle_2015_reachability_free_flow_Xmin.npy`: Loop Free-flow Reachability Matrix during X minites' drive. 13 | * `nodes_loop_mp_list.csv`: List of loop detectors' milepost, with the same order of that in the Loop Speed Matrix. 14 | 15 | A demo of the speed_matrix_2015 is shown as the following figure. The horizontal header denotes the milepost and the vertical header indicates the timestamps. 16 | > 17 | 18 | The name of each milepost header contains 11 characters: 19 | * 1 char: 'd' or 'i', i.e. decreasing direction or increasing direction. 20 | * 2-4 chars: route name, e.g. '405' demonstrates the route I-405. 21 | * 5-6 chars: 'es' has no meanings here. 22 | * 7-11 chars: milepost, e.g. '15036' demonstrates the 150.36 milepost. 23 | 24 | ### Update (2021 Jan.) 25 | Three Seattle loop detector datasets (pickled files) are added to the download link. The formats of the three files is similar to the speed matrix file. 26 | * `volume_avg_matrix_2015`: containing the **averaged volume** over all lanes of a road segment (a set of loop detectors) 27 | * `volume_total_matrix_2015`: containing the **total volume** information (total volume = averaged volume * lane number) 28 | * `occupancy_avg_matrix_2015`: containning the **averaged occupancy** information. 29 | --- 30 | ### Data Download Link: [Seattle Loop Dataset](https://drive.google.com/drive/folders/1E-rRwIPFDZcTWc7zZDcyd4XbIgecW97q?usp=sharing) 31 | 32 | --- 33 | #### If you use this dataset in your work, please cite the following reference: 34 | ###### Reference: 35 | * `Cui, Z., Ke, R., & Wang, Y. (2018). Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction. arXiv preprint arXiv:1801.02143.` 36 | * `Cui, Z., Henrickson, K., Ke, R., & Wang, Y. (2019). Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. IEEE Transactions on Intelligent Transportation Systems.` 37 | ###### BibTex: 38 | ``` 39 | @article{cui2018deep, 40 | title={Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction}, 41 | author={Cui, Zhiyong and Ke, Ruimin and Wang, Yinhai}, 42 | journal={arXiv preprint arXiv:1801.02143}, 43 | year={2018} 44 | } , 45 | @article{cui2019traffic, 46 | title={Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting}, 47 | author={Cui, Zhiyong and Henrickson, Kristian and Ke, Ruimin and Wang, Yinhai}, 48 | journal={IEEE Transactions on Intelligent Transportation Systems}, 49 | year={2019}, 50 | publisher={IEEE} 51 | } 52 | ``` 53 | #### Note: This dataset should only be used for research. 54 | --------------------------------------------------------------------------------