Detecting Anomaly in Traffic Flow from Road Similarity Analysis

Handle URI:
http://hdl.handle.net/10754/611754
Title:
Detecting Anomaly in Traffic Flow from Road Similarity Analysis
Authors:
Liu, Xinran; Liu, Xingwu; Wang, Yuanhong; Pu, Juhua; Zhang, Xiangliang ( 0000-0002-3574-5665 )
Abstract:
Taxies equipped with GPS devices are considered as 24-hour moving sensors widely distributed in urban road networks. Plenty of accurate and realtime trajectories of taxi are recorded by GPS devices and are commonly studied for understanding traffic dynamics. This paper focuses on anomaly detection in traffic volume, especially the non-recurrent traffic anomaly caused by unexpected or transient incidents, such as traffic accidents, celebrations and disasters. It is important to detect such sharp changes of traffic status for sensing abnormal events and planning their impact on the smooth volume of traffic. Unlike existing anomaly detection approaches that mainly monitor the derivation of current traffic status from history in the past, the proposed method in this paper evaluates the abnormal score of traffic on one road by comparing its current traffic volume with not only its historical data but also its neighbors. We define the neighbors as the roads that are close in sense of both geo-location and traffic patterns, which are extracted by matrix factorization. The evaluation results on trajectories data of 12,286 taxies over four weeks in Beijing show that our approach outperforms other baseline methods with higher precision and recall.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Springer Nature
Journal:
Lecture Notes in Computer Science
Issue Date:
2-Jun-2016
DOI:
10.1007/978-3-319-39958-4_8
Type:
Book Chapter
ISSN:
0302-9743
ISBN:
978-3-319-39957-7
Sponsors:
This work was supported by the National Department Public Benefit Research Foundation of China (No.201510209).
Additional Links:
http://link.springer.com/chapter/10.1007%2F978-3-319-39958-4_8
Appears in Collections:
Book Chapters; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLiu, Xinranen
dc.contributor.authorLiu, Xingwuen
dc.contributor.authorWang, Yuanhongen
dc.contributor.authorPu, Juhuaen
dc.contributor.authorZhang, Xiangliangen
dc.date.accessioned2016-06-05T14:26:40Z-
dc.date.available2016-06-05T14:26:40Z-
dc.date.issued2016-06-02-
dc.identifier.isbn978-3-319-39957-7-
dc.identifier.issn0302-9743-
dc.identifier.doi10.1007/978-3-319-39958-4_8-
dc.identifier.urihttp://hdl.handle.net/10754/611754-
dc.description.abstractTaxies equipped with GPS devices are considered as 24-hour moving sensors widely distributed in urban road networks. Plenty of accurate and realtime trajectories of taxi are recorded by GPS devices and are commonly studied for understanding traffic dynamics. This paper focuses on anomaly detection in traffic volume, especially the non-recurrent traffic anomaly caused by unexpected or transient incidents, such as traffic accidents, celebrations and disasters. It is important to detect such sharp changes of traffic status for sensing abnormal events and planning their impact on the smooth volume of traffic. Unlike existing anomaly detection approaches that mainly monitor the derivation of current traffic status from history in the past, the proposed method in this paper evaluates the abnormal score of traffic on one road by comparing its current traffic volume with not only its historical data but also its neighbors. We define the neighbors as the roads that are close in sense of both geo-location and traffic patterns, which are extracted by matrix factorization. The evaluation results on trajectories data of 12,286 taxies over four weeks in Beijing show that our approach outperforms other baseline methods with higher precision and recall.en
dc.description.sponsorshipThis work was supported by the National Department Public Benefit Research Foundation of China (No.201510209).en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/chapter/10.1007%2F978-3-319-39958-4_8en
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-39958-4_8.en
dc.titleDetecting Anomaly in Traffic Flow from Road Similarity Analysisen
dc.typeBook Chapteren
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalLecture Notes in Computer Scienceen
dc.eprint.versionPost-printen
dc.contributor.institutionThe State Key Laboratory of Software Development Environment, Beihang University, Beijing, Chinaen
dc.contributor.institutionInstitute of Computing Technology Chinese Academy of Sciences, Beijing, Chinaen
dc.contributor.institutionResearch Institute of Beihang University in Shenzhen, Shenzhen, Chinaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorZhang, Xiangliangen
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