UM
Detecting taxi speeding from sparse and low-sampled trajectory data
Zhou, Xibo1,2,3,4; Luo, Qiong1; Zhang, Dian4; Ni, Lionel M.5
2018
Conference Name2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10988 LNCS
Pages214-222
Conference Date7 23, 2018 - 7 25, 2018
Conference PlaceMacau, China
Author of SourceSpringer Verlag
AbstractTaxis are a major means of public transportation in large cities, and speeding is a common problem among motor vehicles, including taxis. Unless caught by sensors or patrol officers, many speeding incidents go unnoticed, which pose potential threat to road safety. In this paper, we propose to detect speeding behaviors of individual taxis from taxi trajectory data. Such detection results are useful for driver risk analysis and road safety management. However, the taxi trajectory data are geographically sparse and the sample rate is low. Furthermore, existing methods mainly deal with the estimation of collective road speeds whereas we focus on the speeds of individual vehicles. As such, we propose to use a two-fold collective matrix factorization (CMF)-based model to estimate the individual vehicle speed. We have evaluated our method on real-world datasets, and the results show the effectiveness of our method in detecting taxi speeding behaviors. © 2018, Springer International Publishing AG, part of Springer Nature.
DOI10.1007/978-3-319-96893-3_16
Language英语
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong;
2.Guangzhou HKUST Fok Ying Tung Research Institute, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong;
3.Guangdong Key Laboratory of Popular High Performance Computers, Shenzhen, China;
4.Shenzhen Key Laboratory of Service Computing and Applications, Shenzhen, China;
5.University of Macau, Macau, China
Recommended Citation
GB/T 7714
Zhou, Xibo,Luo, Qiong,Zhang, Dian,et al. Detecting taxi speeding from sparse and low-sampled trajectory data[C]//Springer Verlag,2018:214-222.
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