Map-Matching Error Identification in the Absence of Ground Truth

Abstract

Map-matching of trajectory data has widespread applications in vehicle tracking, traffic flow analysis, route planning, and intelligent transportation systems. Map-matching algorithms snap a set of trajectory points observed by a satellite navigation system to the most likely route segments of a map. However, due to the unavoidable errors in the recorded trajectory points and the incomplete map data, map-matching algorithms may match points to incorrect segments, leading to map-matching errors. Identification of these map-matching errors in the absence of ground truth can only be achieved by visual inspection and reasoning. Thus, the identification of map-matching errors without ground truth is a time-consuming and mundane task. Although research has focused on improving map-matching algorithms, to our knowledge no attempts have been made to automatically classify and identify the residual map-matching errors. In this work, we propose the first method to automatically identify map-matching errors in the absence of ground truth, i.e., only using the recorded trajectory points and the map-matched route. We have evaluated our method on a public dataset and observed an average accuracy of 91% in automatically identifying map-matching errors, thus helping analysts to significantly reduce manual effort for map-matching quality assurance.

A review of map-matching algorithms

“…We can use the Levenshtein distance to determine the number of mismatches between the measurements and the actual route on the map. [4,23] explains how GPS errors can be minimized by trying to find the most suitable candidate points on a route.…”