The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
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.
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Cites
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Cited by
Cross-Area Travel Time Uncertainty Estimation From Trajectory Data: A Federated Learning Approach
Zhu, Y., Ye, Y., Liu, Y., & Yu, J. J. Q. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3203457 · Cited by 35
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation
Zhang, Z., Wang, H., Fan, Z., Chen, J., Song, X., & Shibasaki, R. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3190864 · Cited by 20
Metadata
- Authors
- Yang, C., & Gidófalvi, G.
- Venue
- International Journal of Geographical Information Science
- Publisher
- Informa UK Limited
- Published
- 2017-11-13
- Volume
- 32
- Issue
- 3
- Pages
- 547-570
- ISSN
- 0269-3798 (ISSN-L)
- 1365-8816
- 1362-3087
- OpenAlex
- W2768256553
- Cited by
- 267
- References
- 22
- FWCI
- 6.77
- Type
- journal article
- Language
- en
- Open access
- closed
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