├── .gitignore ├── README.md ├── img ├── demo1.gif ├── demo2.gif ├── demo3.gif ├── demo4.gif ├── dual.png ├── jupyter-demo.png ├── notebook.png ├── raw_data.png └── single.png └── stockholm_boundary.geojson /.gitignore: -------------------------------------------------------------------------------- 1 | ### Data and output 2 | 3 | data/ 4 | output/ 5 | 6 | *.pgw 7 | 8 | # Jupyternotebook 9 | .ipynb_checkpoints 10 | */.ipynb_checkpoints/* 11 | 12 | *.pyc 13 | 14 | *.DS_Store 15 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Map matching on OpenStreetMap, a practical tutorial 2 | 3 | This tutorial is based on the [fast map matching program](https://github.com/cyang-kth/fmm). 4 | It also supports road network in ESRI Shapefile. 5 | 6 | ### Demonstration 7 | 8 | 9 | 10 | ### Content 11 | 12 | - [1. Download routable network from OSM](#1-download-routable-network-from-osm) 13 | - [2. Run map matching with fmm](#2-run-map-matching-with-fmm) 14 | 15 | ### Tools used 16 | 17 | - [OSMNX](https://github.com/gboeing/osmnx) for downloading OSM data 18 | - [Fast map matching program (Python extension)](https://github.com/cyang-kth/fmm) for map matching 19 | 20 | ### 1. Download routable network from OSM 21 | 22 | [OSMNX](https://github.com/gboeing/osmnx) provides a convenient way to download a routable shapefile directly in python. 23 | 24 | The original osmnx saves network in an undirected way, therefore we use the script below to save the graph to a bidirectional network where all edges are directed and bidirectional edges (osm tag one-way is false) are represented by two reverse edges. 25 | 26 |
27 | 28 |
29 | 30 | ```python 31 | import osmnx as ox 32 | import time 33 | from shapely.geometry import Polygon 34 | import os 35 | import numpy as np 36 | 37 | def save_graph_shapefile_directional(G, filepath=None, encoding="utf-8"): 38 | # default filepath if none was provided 39 | if filepath is None: 40 | filepath = os.path.join(ox.settings.data_folder, "graph_shapefile") 41 | 42 | # if save folder does not already exist, create it (shapefiles 43 | # get saved as set of files) 44 | if not filepath == "" and not os.path.exists(filepath): 45 | os.makedirs(filepath) 46 | filepath_nodes = os.path.join(filepath, "nodes.shp") 47 | filepath_edges = os.path.join(filepath, "edges.shp") 48 | 49 | # convert undirected graph to gdfs and stringify non-numeric columns 50 | gdf_nodes, gdf_edges = ox.utils_graph.graph_to_gdfs(G) 51 | gdf_nodes = ox.io._stringify_nonnumeric_cols(gdf_nodes) 52 | gdf_edges = ox.io._stringify_nonnumeric_cols(gdf_edges) 53 | # We need an unique ID for each edge 54 | gdf_edges["fid"] = np.arange(0, gdf_edges.shape[0], dtype='int') 55 | # save the nodes and edges as separate ESRI shapefiles 56 | gdf_nodes.to_file(filepath_nodes, encoding=encoding) 57 | gdf_edges.to_file(filepath_edges, encoding=encoding) 58 | 59 | print("osmnx version",ox.__version__) 60 | 61 | # Download by a bounding box 62 | bounds = (17.4110711999999985,18.4494298999999984,59.1412578999999994,59.8280297000000019) 63 | x1,x2,y1,y2 = bounds 64 | boundary_polygon = Polygon([(x1,y1),(x2,y1),(x2,y2),(x1,y2)]) 65 | G = ox.graph_from_polygon(boundary_polygon, network_type='drive') 66 | start_time = time.time() 67 | save_graph_shapefile_directional(G, filepath='./network-new') 68 | print("--- %s seconds ---" % (time.time() - start_time)) 69 | 70 | # Download by place name 71 | place ="Stockholm, Sweden" 72 | G = ox.graph_from_place(place, network_type='drive', which_result=2) 73 | save_graph_shapefile_directional(G, filepath='stockholm') 74 | 75 | # Download by a boundary polygon in geojson 76 | import osmnx as ox 77 | from shapely.geometry import shape 78 | json_file = open("stockholm_boundary.geojson") 79 | import json 80 | data = json.load(json_file) 81 | boundary_polygon = shape(data["features"][0]['geometry']) 82 | G = ox.graph_from_polygon(boundary_polygon, network_type='drive') 83 | save_graph_shapefile_directional(G, filepath='stockholm') 84 | ``` 85 | 86 | In the third manner, here is a screenshot of the network in QGIS. 87 | 88 |
89 | 90 |
91 | 92 | ### 2. Run map matching with fmm 93 | 94 | Install the [**fmm** program](https://github.com/cyang-kth/fmm) in **C++ and Python** extension following the [instructions](https://github.com/cyang-kth/fmm/wiki). 95 | 96 | The network downloaded from OSMNX using the above script is compatible with fmm 97 | as it contains 98 | - fid: id of edge 99 | - u: source node of an edge 100 | - v: target node of an edge 101 | 102 | Below we show running fmm in Python to match GPS trajectory to the network. 103 | 104 | ```python 105 | from fmm import FastMapMatch,Network,NetworkGraph,UBODTGenAlgorithm,UBODT,FastMapMatchConfig 106 | 107 | ### Read network data 108 | 109 | network = Network("network/edges.shp","fid","u","v") 110 | print "Nodes {} edges {}".format(network.get_node_count(),network.get_edge_count()) 111 | graph = NetworkGraph(network) 112 | 113 | 114 | ### Precompute an UBODT table 115 | 116 | # Can be skipped if you already generated an ubodt file 117 | ubodt_gen = UBODTGenAlgorithm(network,graph) 118 | status = ubodt_gen.generate_ubodt("network/ubodt.txt", 0.02, binary=False, use_omp=True) 119 | print status 120 | 121 | ### Read UBODT 122 | 123 | ubodt = UBODT.read_ubodt_csv("network/ubodt.txt") 124 | 125 | ### Create FMM model 126 | model = FastMapMatch(network,graph,ubodt) 127 | 128 | ### Define map matching configurations 129 | 130 | k = 8 131 | radius = 0.003 132 | gps_error = 0.0005 133 | fmm_config = FastMapMatchConfig(k,radius,gps_error) 134 | 135 | 136 | ### Run map matching for wkt 137 | wkt = "LineString(104.10348 30.71363,104.10348 30.71363,104.10348 30.71363,104.10348 30.71363,104.10348 30.71363)" 138 | result = model.match_wkt(wkt,fmm_config) 139 | 140 | ### Print map matching result 141 | print "Opath ",list(result.opath) 142 | print "Cpath ",list(result.cpath) 143 | print "WKT ",result.mgeom.export_wkt() 144 | ``` 145 | 146 | A more complete version of notebook (including matching GPS data stored in files) 147 | can be found at https://github.com/cyang-kth/fmm/blob/master/example/notebook/fmm_example.ipynb. 148 | 149 | STMATCH algorithm which does not need precomputation can also be used. https://github.com/cyang-kth/fmm/blob/master/example/notebook/stmatch_example.ipynb 150 | 151 | ### Citation information 152 | 153 | Please cite fmm in your publications if it helps your research: 154 | 155 | ``` 156 | Can Yang & Gyozo Gidofalvi (2018) Fast map matching, an algorithm 157 | integrating hidden Markov model with precomputation, International Journal of Geographical Information Science, 32:3, 547-570, DOI: 10.1080/13658816.2017.1400548 158 | ``` 159 | 160 | Bibtex format 161 | 162 | ``` 163 | @article{Yang2018fast, 164 | author = {Can Yang and Gyozo Gidofalvi}, 165 | title = {Fast map matching, an algorithm integrating hidden Markov model with precomputation}, 166 | journal = {International Journal of Geographical Information Science}, 167 | volume = {32}, 168 | number = {3}, 169 | pages = {547-570}, 170 | year = {2018}, 171 | publisher = {Taylor & Francis}, 172 | doi = {10.1080/13658816.2017.1400548}, 173 | } 174 | ``` 175 | 176 | ### Contact information 177 | 178 | Can Yang, Ph.D. student at KTH, Royal Institute of Technology in Sweden 179 | 180 | Email: cyang(at)kth.se 181 | 182 | Homepage: https://people.kth.se/~cyang/ 183 | -------------------------------------------------------------------------------- /img/demo1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cyang-kth/osm_mapmatching/4c09aec2f5fb40f3002dd55a6c40e2a7c625fada/img/demo1.gif -------------------------------------------------------------------------------- /img/demo2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cyang-kth/osm_mapmatching/4c09aec2f5fb40f3002dd55a6c40e2a7c625fada/img/demo2.gif -------------------------------------------------------------------------------- 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