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

作者：Chen, Chao^*; Ding, Yan; Xie, Xuefeng^*; Zhang, Shu

来源：Journal of Ambient Intelligence and Humanized Computing, 2018, 9(5): 1623-1633.

DOI：10.1007/s12652-018-0760-0

• Intelligent transportation
• Map-matching
• Road network
• Heading direction
• Shortest path finding

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.

• 出版日期2018-10
• 单位重庆大学