Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory

Zhengang Xiong, Bin Li and Dongmei Liu
Additional contact information
Zhengang Xiong: Research Institute of Highway Ministry of Transport, Beijing 100088, China
Bin Li: Research Institute of Highway Ministry of Transport, Beijing 100088, China
Dongmei Liu: Research Institute of Highway Ministry of Transport, Beijing 100088, China

Sustainability, 2021, vol. 13, issue 22, 1-15

Abstract: In the field of map matching, algorithms using topological relationships of road networks along with other data are normally suitable for high frequency trajectory data. However, for low frequency trajectory data, the above methods may cause problems of low matching accuracy. In addition, most past studies only use information from the road network and trajectory, without considering the traveler’s path choice preferences. In order to address the above-mentioned issue, we propose a new map matching method that combines the widely used Hidden Markov Model (HMM) with the path choice preference of decision makers. When calculating transition probability in the HMM, in addition to shortest paths and road network topology relationships, the choice preferences of travelers are also taken into account. The proposed algorithm is tested using sparse and noisy trajectory data with four different sampling intervals, while compared the results with the two underlying algorithms. The results show that our algorithm can improve the matching accuracy, especially for higher frequency locating trajectory. Importantly, the method takes into account the route choice preferences while correcting deviating trajectory points to the corresponding road segments, making the assumptions more reasonable. The case-study is in the city of Beijing, China.

Keywords: map matching; Hidden Markov Model; route choice preference; low sampling frequency; GPS (Global Positioning System) trajectory (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/22/12820/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/22/12820/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:22:p:12820-:d:683179

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().