https://github.com/categulario/map_matching
Algorithms to find the streets that a vehicle should have traveled to generate a given GPS track
https://github.com/categulario/map_matching
algorithm map-matching redis street
Last synced: about 1 month ago
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Algorithms to find the streets that a vehicle should have traveled to generate a given GPS track
- Host: GitHub
- URL: https://github.com/categulario/map_matching
- Owner: categulario
- License: gpl-3.0
- Created: 2017-05-13T20:45:52.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2022-09-22T19:02:41.000Z (over 2 years ago)
- Last Synced: 2025-03-27T07:11:52.527Z (about 2 months ago)
- Topics: algorithm, map-matching, redis, street
- Language: Python
- Homepage:
- Size: 1.02 MB
- Stars: 60
- Watchers: 4
- Forks: 21
- Open Issues: 5
-
Metadata Files:
- Readme: README.rst
- License: LICENSE.txt
Awesome Lists containing this project
README
Map-Matching Algorithm
######################
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**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:
.. image:: https://categulario.xyz/map_matching_result.png
:target: https://categulario.xyz/map_matching_result.png
:alt: Output of the example run
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:
.. code:: bash
$ pip install mapmatching
Or install from source:
.. code:: bash
$ cd mapmatching
$ python setup.py install
CLI Usage
---------
Download data from OpenStreetMaps:
.. code:: bash
$ 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.
.. code:: bash
$ mapmatching load -f streets.json
The two previous commands can be chained:
.. code:: bash
$ 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.
.. code:: bash
$ mapmatching match -h
$ mapmatching match data/route.geojson -o output.json
Optionally visualize it in the browser:
.. code:: bash
$ 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.
.. code:: python
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)