Paper Info
Title
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.
Year
DOI
Venue
2018
10.1007/s12652-018-0760-0
J. Ambient Intelligence and Humanized Computing
Keywords
Field
DocType
Intelligent transportation, Map-matching, Road network, Heading direction, Shortest path finding
Data mining,Computer science,Algorithm,Exploit,Global Positioning System,Gps trajectory,Network data,Intelligent transportation system,Map matching,Beijing,Computation
Journal
Volume
Issue
ISSN
9
5
1868-5145
Citations 
PageRank 
References 
3
0.53
20
Authors
4
Name
Order
Citations
PageRank
Chao Chen18711.13
Yan Ding24012.03
Xuefeng Xie331.54
Shu Zhang4188.92