Map Matching based on GraphHopper i.e. 'snap' GPX records to digital roads
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README.md

Map Matching based on GraphHopper

Build Status

Map matching is the process to match a sequence of real world coordinates into a digital map. Read more at Wikipedia. It can be used for tracking vehicles' GPS information, important for further digital analysis. Or e.g. attaching turn instructions for any recorded GPX route.

Currently this project is under heavy development but produces already good results for various use cases. Let us know if not and create an issue!

Map Matching Illustration

License

Apache License 2.0

Discussion

Discussion happens here.

Installation and Usage

Java and Maven are required.

Then you need to import the area you want to do map-matching on:

./map-matching.sh action=import datasource=./some-dir/osm-file.pbf [vehicle=car]

The parameter vehicle defines the routing profile like bike, motorcycle or foot. For all supported values see the variables in the EncodingManager class of GraphHopper.

If you have already imported a datasource with a specific profile, you need to remove the folder graph-cache in your map-matching root directory.

Now you can do these matches:

./map-matching.sh action=match gpx=./track-data/.*gpx

Possible arguments are:

instructions=de             # default=, type=String, if an country-iso-code (like en or de) is specified turn instructions are included in the output, leave empty or default to avoid this
gpxAccuracy=15              # default=15, type=int, unit=meter, the precision of the used device
separatedSearchDistance=500 # default=500, type=int, unit=meter, we split the incoming list into smaller parts (hopefully) without loops. Later we'll detect loops and insert the correctly detected road recursivly, see #1
maxSearchMultiplier=50      # default=50, type=int, the limit we use to search a route from one gps entry to the other to avoid exploring the whole graph in case of disconnected subnetworks. See #15
forceRepair=false           # default=false, type=boolean, when merging two path segments it can happen that edges seem illegal like two adjacent and parallel edges and the search will normally fail. Setting this to true tries to clean the illegal situation

This will produce gpx results similar named as the input files.

Java usage

Or use this Java snippet:

// import OpenStreetMap data
GraphHopper hopper = new GraphHopper();
hopper.setOSMFile("./map-data/leipzig_germany.osm.pbf");
hopper.setGraphHopperLocation("./target/mapmatchingtest");
CarFlagEncoder encoder = new CarFlagEncoder();
hopper.setEncodingManager(new EncodingManager(encoder));
hopper.setCHEnable(false);
hopper.importOrLoad();

// create MapMatching object, can and should be shared accross threads

GraphHopperStorage graph = hopper.getGraphHopperStorage();
LocationIndexMatch locationIndex = new LocationIndexMatch(graph,
                (LocationIndexTree) hopper.getLocationIndex());
MapMatching mapMatching = new MapMatching(graph, locationIndex, encoder);

// do the actual matching, get the GPX entries from a file or via stream
List<GPXEntry> inputGPXEntries = new GPXFile().doImport("nice.gpx").getEntries();
MatchResult mr = mapMatching.doWork(inputGPXEntries);

// return GraphHopper edges with all associated GPX entries
List<EdgeMatch> matches = mr.getEdgeMatches();
// now do something with the edges like storing the edgeIds or doing fetchWayGeometry etc
matches.get(0).getEdgeState();

with this maven dependency:

<dependency>
    <groupId>com.graphhopper</groupId>
    <artifactId>map-matching</artifactId>
    <!-- or 0.6-SNAPSHOT for the unstable -->
    <version>0.5.0.0</version>
</dependency>

Later we will add a simple web service

UI to visually compare

There is a simple UI taken from makinacorpus/Leaflet.FileLayer where you can load your input and output gpx files to compare the results. Some GPX seem to fail when trying to load them.

Start e.g. via 'firefox simple-js-ui/index.html'

Note

Note that the edge and node IDs from GraphHopper will change for different PBF files, like when updating the OSM data.

About

The used algorithm is explained in this blog post.