xiaomo123zk/map_matching

Algorithms to find the streets that a vehicle should have traveled to generate a given GPS track

Map-Matching Algorithm

Im rewriting this in the Rust programming language: check the progress here:

https://gitlab.com/categulario/mapmatching-rs

My implementation of the map matching algorithm from this article (Althought with some modifications). The goal is to get the streets from a gps track.

This is how it looks like:

The gray line is the gps trace and the colored lines describe the map-matched most-likely route in the streets for the vehicle.

For reference read the resulting article.

Setup

You'll need python 3.5+ and a redis server running. The usage of a virtual environment is recommended.

Install from pypi:

$ pip install mapmatching

Or install from source:

$ cd mapmatching
$ python setup.py install

CLI Usage

Download data from OpenStreetMaps:

$ mapmatching download -h
$ mapmatching download -96.99107360839844 19.441181182861328 -96.846435546875 19.59616470336914 -o streets.json

And load it to redis, by default it loads it to database 1 instead of redis default of 0.

$ mapmatching load -f streets.json

The two previous commands can be chained:

$ mapmatching download -96.99107360839844 19.441181182861328 -96.846435546875 19.59616470336914 | mapmatching load

Then run the match task with a geojson file with a single gps track. A sample track that works with the sample bounding box is contained in the data/ directory of the repository.

$ mapmatching match -h
$ mapmatching match data/route.geojson -o output.json

Optionally visualize it in the browser:

$ pip install geojsonio
$ geojsonio output.json

if the output is too big you might need to copy+paste the contents of the output file into http://geojson.io

Python API

You can also import this as a module and use it in your python code. You'll still need a running redis instance.

import json

from redis import Redis

from mapmatching.match import match
from mapmatching.lua import LuaManager
from mapmatching.data import download_from_overpass, load_to_redis

data = download_from_overpass(-96.99107360839844, 19.441181182861328, -96.846435546875, 19.59616470336914)

redis = Redis(host='localhost', port='6379', db=0)

load_to_redis(data, redis)

with open('data/route.geojson', 'r') as routefile:
   route = json.load(routefile)

coordinates = route['features'][0]['geometry']['coordinates']

json_output = match(
   redis,
   LuaManager(redis),
   coordinates,
   10,  # How many points to process
   50,  # Radius in meters to use in the search for close points
)

with open('output.json', 'w') as outputfile:
   json.dump(json_output, outputfile, indent=2)