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

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...

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Autores principales:	Zhengang Xiong, Bin Li, Dongmei Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	map matching Hidden Markov Model route choice preference low sampling frequency GPS (Global Positioning System) trajectory Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/2696185a319d4770a52215401a4a2d9f
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id	oai:doaj.org-article:2696185a319d4770a52215401a4a2d9f
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spelling	oai:doaj.org-article:2696185a319d4770a52215401a4a2d9f2021-11-25T19:04:34ZMap-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory10.3390/su1322128202071-1050https://doaj.org/article/2696185a319d4770a52215401a4a2d9f2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12820https://doaj.org/toc/2071-1050In 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.Zhengang XiongBin LiDongmei LiuMDPI AGarticlemap matchingHidden Markov Modelroute choice preferencelow sampling frequencyGPS (Global Positioning System) trajectoryEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12820, p 12820 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	map matching Hidden Markov Model route choice preference low sampling frequency GPS (Global Positioning System) trajectory Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	map matching Hidden Markov Model route choice preference low sampling frequency GPS (Global Positioning System) trajectory Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Zhengang Xiong Bin Li Dongmei Liu Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory
description	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.
format	article
author	Zhengang Xiong Bin Li Dongmei Liu
author_facet	Zhengang Xiong Bin Li Dongmei Liu
author_sort	Zhengang Xiong
title	Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory
title_short	Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory
title_full	Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory
title_fullStr	Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory
title_full_unstemmed	Map-Matching Using Hidden Markov Model and Path Choice Preferences under Sparse Trajectory
title_sort	map-matching using hidden markov model and path choice preferences under sparse trajectory
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2696185a319d4770a52215401a4a2d9f
work_keys_str_mv	AT zhengangxiong mapmatchingusinghiddenmarkovmodelandpathchoicepreferencesundersparsetrajectory AT binli mapmatchingusinghiddenmarkovmodelandpathchoicepreferencesundersparsetrajectory AT dongmeiliu mapmatchingusinghiddenmarkovmodelandpathchoicepreferencesundersparsetrajectory
_version_	1718410328391286784

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

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