Evaluation of map‐matching algorithms for smartphone‐based active travel data

Abstract

Global Positioning System (GPS) data on walking and cycling trips can generate useful insights for transportation systems but require substantial processing. One of the key GPS data processing steps is “map‐matching”, or inference of the sequence of network links traversed during travel. The objective of this research is to evaluate the accuracy of existing map‐matching algorithms for GPS data on active travel. A method to flag erroneous map‐matching results without requiring ground‐truth data and improvements for active travel data are also proposed. Six map‐matching algorithms are applied to a sample of 63 trajectories, stratified on network density and average heading change, extracted from a large set of real‐world trips from metropolitan Vancouver, Canada. Results show that the best performing method is PgMapMatch, which can be further improved by adjustments to link costs and allowing wrong‐way travel. Two other algorithms have similarly accurate routes (70–90% accuracy, depending on the measure), but fail to generate routes for about a third of trips. The proposed error detection measure can be used (without ground truth data) to flag matched routes requiring visual inspection, with a recommendation to look for: Wrong‐way travel, missing links in the network data, and parallel facilities on the same street.

By cyclists, for cyclists: Road grade and elevation estimation from crowd-sourced fitness application data

“…These GPS trajectories were map-matched using the PgMapMatch algorithm, developed by Miller-Ball et al [ 44 ], with street network data extracted from OSM using overpassID [ 45 ]. PgMapMatch was selected based on recently demonstrated good performance for cycling GPS data [ 46 ]. We next identified the GPS trajectories that traversed any of our 16 study locations from the map-matched routes.…”