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Map Matching

Snaps GPX traces to the road.

Read more about the map matching problem at Wikipedia.

See the demo in action (black is GPS track, green is matched result):

map-matching-example

Web app

To install GraphHopper and start the GraphHopper server see these instructions. After the import process finished you can access a simple map matching UI via http://localhost:8989/maps/map-matching/ (including the trailing slash). Note that for map-matching you need a version 3.0 or higher so you probably need to build from source currently.

You can post GPX files and get back snapped results as GPX or as JSON. An example curl request is:

curl -XPOST -H "Content-Type: application/gpx+xml" -d @web/src/test/resources/test1.gpx "localhost:8989/match?profile=car&type=json"

CLI usage

You can also use map-matching via the command line without running the GraphHopper server. The usage is very similar to the GraphHopper server. You need a configuration file and running the match command will either use existing GraphHopper files or trigger a new import. Use the match command like this for example:

java -jar graphhopper-web-3.0-SNAPSHOT.jar match --file config.yml --profile car web/src/test/resources/*.gpx

where the argument after -jar is the GraphHopper jar that you need to build from source or download (3.0 or higher). The profile is chosen via the --profile option and the GPX files are specified after the last option. In the above example we use all GPX files found in the test resources.

Java usage

Have a look at MapMatchingResource.java to see how the web service is implemented on top of library functions to get an idea how to use map matching in your own project.

Use this Maven dependency:

<dependency>
    <groupId>com.graphhopper</groupId>
    <artifactId>graphhopper-map-matching</artifactId>
    <version>3.0-SNAPSHOT</version>
</dependency>

Note

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

About

The map matching algorithm mainly follows the approach described in

Newson, Paul, and John Krumm. "Hidden Markov map matching through noise and sparseness." Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2009.

This algorithm works as follows. For each input GPS position, a number of map matching candidates within a certain radius around the GPS position is computed. The Viterbi algorithm as provided by the hmm-lib is then used to compute the most likely sequence of map matching candidates. Thereby, the distances between GPS positions and map matching candidates as well as the routing distances between consecutive map matching candidates are taken into account. The GraphHopper routing engine is used to find candidates and to compute routing distances.