Detection of traffic incidents using nonlinear time series analysis

dc.creator	Fragkou A.D., Karakasidis T.E., Nathanail E.	en
dc.date.accessioned	2023-01-31T07:38:53Z
dc.date.available	2023-01-31T07:38:53Z
dc.date.issued	2018
dc.identifier	10.1063/1.5024924
dc.identifier.issn	10541500
dc.identifier.uri	http://hdl.handle.net/11615/71784
dc.description.abstract	In this study, we present results of the application of nonlinear time series analysis on traffic data for incident detection. More specifically, we analyze daily volume records of Attica Tollway (Greece) collected from sensors located at various locations. The analysis was performed using the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) method of the volume data of the lane closest to the median. The results show that it is possible to identify, through the abrupt change of the dynamics of the system revealed by RPs and RQA, the occurrence of incidents on the freeway and differentiate from recurrent traffic congestion. The proposed methodology could be of interest for big data traffic analysis. © 2018 Author(s).	en
dc.language.iso	en	en
dc.source	Chaos	en
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048325913&doi=10.1063%2f1.5024924&partnerID=40&md5=403e2643d5b1197f8ae0e3b60c110190
dc.subject	American Institute of Physics Inc.	en
dc.title	Detection of traffic incidents using nonlinear time series analysis	en
dc.type	journalArticle	en

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