Example

Map Matching

The noiseplanet package provides different tools for matching a track to the Open Street Map network.

  • matching to the nearest edge,
  • hmm based matching. To match a track, composed by latitudes and longitudes, use :

Firt, import the following packages :

import numpy as np
import osmnx as ox
from noiseplanet import matcher

import numpy as np
import osmnx as ox
from noiseplanet import matching

track = np.array([[45.7584882 ,  4.83585996],
                [45.75848068,  4.83586747],
                [45.75849549,  4.83585205],
                [45.75849134,  4.83584647],
                [45.75848135,  4.8358245 ],
                # ...
                [45.75846756,  4.83580848],
                [45.75844998,  4.83580936],
                [45.7584067 ,  4.83580086],
                [45.7584067 ,  4.83580086],
                [45.75839346,  4.83579883]])

graph = matching.model.graph_from_track(track)

track_coor, route_corr, edgeid, stats = matching.match(graph, track, method='hmm')

And visualize the results :

from noiseplanet.ui import plot_html

# Plot the graph
plot_html(track, track_corr=track_corr, route_corr=route_corr,
        proj=True, show_graph=True)
alternate text

You can change the matching method. For example, the above method uses Hidden Markov Models, and is the best way to match a track on the OSM (see the report for more details). However, you can use a naive method to match the track on the nearest road :

track_coor, route_corr, edgeid, stats = matcher.match(graph, track, method='nearest')

And visualize the results :

from noiseplanet.ui import plot_html

# Plot the graph
plot_html(track, track_corr=track_corr, route_corr=route_corr,
        proj=True, show_graph=True)
alternate text