On map-matching vehicle tracking data

TLDR: This work presents three algorithms that consider especially the trajectory nature of the data rather than simply the current position as in the typical map-matching case, and proposes an incremental algorithm that matches consecutive portions of the trajectory to the road network.

Abstract: Vehicle tracking data is an essential "raw" material for a broad range of applications such as traffic management and control, routing, and navigation. An important issue with this data is its accuracy. The method of sampling vehicular movement using GPS is affected by two error sources and consequently produces inaccurate trajectory data. To become useful, the data has to be related to the underlying road network by means of map matching algorithms. We present three such algorithms that consider especially the trajectory nature of the data rather than simply the current position as in the typical map-matching case. An incremental algorithm is proposed that matches consecutive portions of the trajectory to the road network, effectively trading accuracy for speed of computation. In contrast, the two global algorithms compare the entire trajectory to candidate paths in the road network. The algorithms are evaluated in terms of (i) their running time and (ii) the quality of their matching result. Two novel quality measures utilizing the Frechet distance are introduced and subsequently used in an experimental evaluation to assess the quality of matching real tracking data to a road network.