An improved hidden Markov model-based map matching algorithm considering candidate point grouping and trajectory connectivity
文献类型：期刊论文
| 作者 | Li, Bozhao4; Cai, Zhongliang4; Kang, Mengjun4; Su, Shiliang1,4; Jiang, Lili2; Ge, Yong2; Niu, Yanfen3 |
| 刊名 | CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE
 |
| 出版日期 | 2022-11-18 |
| 页码 | 20 |
| ISSN号 | 1523-0406 |
| 关键词 | Map-matching algorithm hidden Markov model Viterbi algorithm floating car data complex urban road environment |
| DOI | 10.1080/15230406.2022.2135023 |
| 通讯作者 | Cai, Zhongliang(zlcai@whu.edu.cn) |
| 英文摘要 | The hidden Markov model-based map matching algorithm (HMM-MM) is an effective method for online vehicle navigation and offline trajectory position correction. Common HMM-MMs are susceptible to the influence of adjacent road segment endpoints and similar parallel roads, because the multi-index probability model may ignore some indexes when the probability of other indexes is high. This makes the map-matching result not meet the assumption that vehicles always travel the shortest or optimal path, and it cannot guarantee that the trajectory points can match to the nearest position of the maximum likelihood road segment, resulting in poor accuracy. In this paper, an IHMM-MM is proposed. IHMM-MM (1) modifies the definition of transition probability and no longer takes the straight-line distance between trajectory points as the reference for the shortest path length between candidate point pairs. (2) supplements the definition of observation probability and introduces the point-line relation function to screen and group candidate points. (3) adds additional logic outside the HMM probability model to consider the trajectory connectivity and fill in the key trajectory points where the vehicles travel. Experiments show that the IHMM-MM can effectively improve the sampling frequency of trajectory data and has better performance in complex urban road environments. |
| WOS研究方向 | Geography |
| 语种 | 英语 |
| 出版者 | TAYLOR & FRANCIS INC |
| WOS记录号 | WOS:000888859500001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187278]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Cai, Zhongliang |
| 作者单位 | 1.Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 3.Beijing PalmGo Infotech Co Ltd, Data Prod Dept, Beijing, Peoples R China 4.Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Bozhao,Cai, Zhongliang,Kang, Mengjun,et al. An improved hidden Markov model-based map matching algorithm considering candidate point grouping and trajectory connectivity[J]. CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE,2022:20. |
| APA | Li, Bozhao.,Cai, Zhongliang.,Kang, Mengjun.,Su, Shiliang.,Jiang, Lili.,...&Niu, Yanfen.(2022).An improved hidden Markov model-based map matching algorithm considering candidate point grouping and trajectory connectivity.CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE,20. |
| MLA | Li, Bozhao,et al."An improved hidden Markov model-based map matching algorithm considering candidate point grouping and trajectory connectivity".CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE (2022):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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