A three-stage online map-matching algorithm by fully using vehicle heading direction

Abstract

Map-matching is essential for almost all intelligent transportation systems, including context and personalized services. To support real-time intelligent transportation services, online map-matching is usually a prerequisite. Although many map-matching methods have been proposed, they often fail to balance the two conflicting objectives, i.e., matching quality and computation time. To alleviate the contradiction, in this paper, we propose a three-stage online map-matching algorithm, named as SD-Matching, to fully exploit a new dimension of collected GPS trajectory data (i.e., vehicle heading direction) in a provably smart way. In the first stage, heading direction is first used to enhance the probability computation of candidate edges for a given GPS point. In the second stage, heading direction is also employed to narrow down the searching space and serve as a cost-effective guider in the shortest path computation for two consecutive GPS points. In the third stage, heading direction is further utilized to refine the vehicle travelling path for a sequence of GPS points, together with the topology of the road network. Finally, we evaluate the SD-Matching algorithm using the real-world taxi data and road network data in the city of Beijing, China, to demonstrate its effectiveness and efficiency.