An Enhanced Hidden Markov Map Matching Model for Floating Car Data
The map matching (MM) model plays an important role in revising the locations of floating car data (FCD) on a digital map. However, most existing MM models have multiple shortcomings, such as a low matching accuracy for complex roads, long running times, an inability to take full advantage of histor...
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Published in | Sensors (Basel, Switzerland) Vol. 18; no. 6; p. 1758 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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31.05.2018
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Abstract | The map matching (MM) model plays an important role in revising the locations of floating car data (FCD) on a digital map. However, most existing MM models have multiple shortcomings, such as a low matching accuracy for complex roads, long running times, an inability to take full advantage of historical FCD information, and challenges in maintaining the topological adjacency and obeying traffic rules. To address these issues, an enhanced hidden Markov map matching (EHMM) model is proposed by adopting explicit topological expressions, using historical FCD information and introducing traffic rules. The EHMM model was validated against areal ground dataset at various sampling intervals and compared with the spatial and temporal matching model and the ordinary hidden Markov matching model. The empirical results reveal that the matching accuracy of the EHMM model is significantly higher than that of the reference models regarding real FCD trajectories at medium and high sampling rates. The running time of the EHMM model was notably shorter than those of the reference models. The matching results of the EHMM model retained topological adjacency and complied with traffic regulations better than the reference models. |
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AbstractList | The map matching (MM) model plays an important role in revising the locations of floating car data (FCD) on a digital map. However, most existing MM models have multiple shortcomings, such as a low matching accuracy for complex roads, long running times, an inability to take full advantage of historical FCD information, and challenges in maintaining the topological adjacency and obeying traffic rules. To address these issues, an enhanced hidden Markov map matching (EHMM) model is proposed by adopting explicit topological expressions, using historical FCD information and introducing traffic rules. The EHMM model was validated against areal ground dataset at various sampling intervals and compared with the spatial and temporal matching model and the ordinary hidden Markov matching model. The empirical results reveal that the matching accuracy of the EHMM model is significantly higher than that of the reference models regarding real FCD trajectories at medium and high sampling rates. The running time of the EHMM model was notably shorter than those of the reference models. The matching results of the EHMM model retained topological adjacency and complied with traffic regulations better than the reference models. |
Author | Zhang, Chi Che, Mingliang Cao, Xinliang Wang, Yingli |
AuthorAffiliation | School of Geographic Science, Nantong University, Nantong 226019, Jiangsu, China; dawnche@163.com (M.C.); benz1983@163.com (C.Z.); cxliang@mail.ustc.edu.cn (X.C.) |
AuthorAffiliation_xml | – name: School of Geographic Science, Nantong University, Nantong 226019, Jiangsu, China; dawnche@163.com (M.C.); benz1983@163.com (C.Z.); cxliang@mail.ustc.edu.cn (X.C.) |
Author_xml | – sequence: 1 givenname: Mingliang surname: Che fullname: Che, Mingliang email: dawnche@163.com organization: School of Geographic Science, Nantong University, Nantong 226019, Jiangsu, China. dawnche@163.com – sequence: 2 givenname: Yingli surname: Wang fullname: Wang, Yingli email: wyl621021@ntu.edu.cn organization: School of Geographic Science, Nantong University, Nantong 226019, Jiangsu, China. wyl621021@ntu.edu.cn – sequence: 3 givenname: Chi surname: Zhang fullname: Zhang, Chi email: benz1983@163.com organization: School of Geographic Science, Nantong University, Nantong 226019, Jiangsu, China. benz1983@163.com – sequence: 4 givenname: Xinliang surname: Cao fullname: Cao, Xinliang email: cxliang@mail.ustc.edu.cn organization: School of Geographic Science, Nantong University, Nantong 226019, Jiangsu, China. cxliang@mail.ustc.edu.cn |
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Keywords | satellite positioning systems traffic regulation floating car data topological adjacency hidden Markov model map matching model |
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Snippet | The map matching (MM) model plays an important role in revising the locations of floating car data (FCD) on a digital map. However, most existing MM models... |
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SubjectTerms | Digital mapping Economic models floating car data hidden Markov model map matching model Markov chains Model accuracy Model matching Sampling satellite positioning systems topological adjacency Traffic models traffic regulation |
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Title | An Enhanced Hidden Markov Map Matching Model for Floating Car Data |
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