Fast map matching, an algorithm integrating hidden Markov model with precomputation

ABSTRACT

Wide deployment of global positioning system (GPS) sensors has generated a large amount of data with numerous applications in transportation research. Due to the observation error, a map matching (MM) process is commonly performed to infer a path on a road network from a noisy GPS trajectory. The increasing data volume calls for the design of efficient and scalable MM algorithms. This article presents fast map matching (FMM), an algorithm integrating hidden Markov model with precomputation, and provides an open-source implementation. An upper bounded origin-destination table is precomputed to store all pairs of shortest paths within a certain length in the road network. As a benefit, repeated routing queries known as the bottleneck of MM are replaced with hash table search. Additionally, several degenerate cases and a problem of reverse movement are identified and addressed in FMM. Experiments on a large collection of real-world taxi trip trajectories demonstrate that FMM has achieved a considerable single-processor MM speed of 25,000–45,000 points/second varying with the output mode. Investigation on the running time of different steps in FMM reveals that after precomputation is employed, the new bottleneck is located in candidate search, and more specifically, the projection of a GPS point to the polyline of a road edge. Reverse movement in the result is also effectively reduced by applying a penalty.

KEYWORDS:

• Map matching
• precomputation
• performance improvement

Acknowledgments

The authors would like to thank the editor and the reviewers for their valuable and constructive comments, which greatly improved the quality of this article. The first author is supported by Chinese Scholarship Council. We are also grateful to Clas Rydegren for providing access to the taxi GPS dataset.

Disclosure statement

No potential conflict of interest was reported by the authors.