Map matching in ArcGIS

Map Matcher

Purpose:

This python script allows map matching (matching of track points to a network) in arcpy using a Hidden Markov model with probabilities parameterized based on spatial + network distances. Follows the ideas in Newson, Krumm (2009): "Hidden markov Map Matching through noise and sparseness"

Example usage under 'main'

Author: Simon Scheider

Created: 17/02/2017 Copyright: (c) simon 2017 Licence:

The code is written in Python 2.7 and depends on:

• arcpy (ships with ArcGIS and its own Python 2.7)
• networkx (# python pip install networkx (https://networkx.github.io)) (note: requires installing GDAL first, which can be obtained as a wheel from http://www.lfd.uci.edu/~gohlke/pythonlibs/ and then installed with pip locally: python pip install GDAL-2.1.3-cp27-cp27m-win32.whl )

Usage

The main method is mapMatch. Based on the Viterbi algorithm for Hidden Markov models, see https://en.wikipedia.org/wiki/Viterbi_algorithm. It gets trackpoints and segments, and returns the most probable segment path (a list of segments) for the list of points.

Inputs:

    @param track = a shape file (filename) representing a track, can also be unprojected (WGS84)
    @param segments = a shape file of network segments, should be projected (in meter) to compute Euclidean distances properly (e.g. GCS Amersfoord)
    @param decayconstantNet (optional) = the network distance (in meter) after which the match probability falls under 0.34 (exponential decay). (note this is the inverse of lambda)
    @param decayConstantEu (optional) = the Euclidean distance (in meter) after which the match probability falls under 0.34 (exponential decay). (note this is the inverse of lambda)
    @param maxDist (optional) = the Euclidean distance threshold (in meter) for taking into account segments candidates.

note:

depending on the type of movement, optional parameters need to be fine tuned to get optimal results.