Simultaneous origin-destination matrix estimation in dynamic traffic networks with evolutionary computing

Tsekeris, T; Dimitriou, L; Stathopoulos, A

dc.contributor.author	Tsekeris, T	en
dc.contributor.author	Dimitriou, L	en
dc.contributor.author	Stathopoulos, A	en
dc.date.accessioned	2014-03-01T02:44:57Z
dc.date.available	2014-03-01T02:44:57Z
dc.date.issued	2007	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32049
dc.subject	Evolutionary computing	en
dc.subject	Microscopic simulation	en
dc.subject	Origindestination matrices	en
dc.subject	Traffic flows	en
dc.subject	Transportation networks	en
dc.subject.other	Flow patterns	en
dc.subject.other	Motor transportation	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Transportation	en
dc.subject.other	Microscopic simulation	en
dc.subject.other	Origindestination matrices	en
dc.subject.other	Traffic flows	en
dc.subject.other	Transportation networks	en
dc.subject.other	Evolutionary algorithms	en
dc.title	Simultaneous origin-destination matrix estimation in dynamic traffic networks with evolutionary computing	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-71805-5_73	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-71805-5_73	en
heal.publicationDate	2007	en
heal.abstract	This paper presents an evolutionary computing approach for the estimation of dynamic Origin-Destination (O-D) trip matrices from automatic traffic counts in urban networks. A multi-objective, simultaneous optimization problem is formulated to obtain a mutually consistent solution between the resulting O-D matrix and the path/link flow loading pattern. A genetically augmented microscopic simulation procedure is used to determine the path flow pattern between each O-D pair by estimating the set of turning proportions at each intersection. The proposed approach circumvents the restrictions associated with employing a user-optimal Dynamic Traffic Assignment (DTA) procedure and provides a stochastic global search of the optimal O-D trip and turning flow distributions. The application of the model into a real arterial street sub-network demonstrates its ability to provide results of satisfactory accuracy within fast computing speeds and, hence, its potential usefulness to support the deployment of dynamic urban traffic management systems. ©Springer- Verlag Berlin Heidelberg 2007.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-71805-5_73	en
dc.identifier.volume	4448 LNCS	en
dc.identifier.spage	668	en
dc.identifier.epage	677	en