Combined genetic computation of microscopic trip demand in urban networks

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	2014-03-01T01:57:06Z
dc.date.available	2014-03-01T01:57:06Z
dc.date.issued	2008	en
dc.identifier.issn	1860949X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28343
dc.subject	Genetic computation	en
dc.subject	Microscopic simulation	en
dc.subject	Traffic flows	en
dc.subject	Trip demand	en
dc.subject	Urban networks	en
dc.title	Combined genetic computation of microscopic trip demand in urban networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/978-3-540-69390-1_1	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-69390-1_1	en
heal.publicationDate	2008	en
heal.abstract	This chapter describes a combined genetic computation approach for estimating time-varying Origin-Destination (O-D) trip demand matrices from traffic counts in urban networks. The estimation procedure combines a microscopic model simulating traffic flow conditions with a genetic algorithm to synthesize the network O-D trip matrix, through determining the turning flow proportions at each intersection. The proposed approach avoids the restrictions involved in employing a user-optimal Dynamic Traffic Assignment (DTA) procedure and carries out a stochastic global search of the optimal O-D trip and turning flow distributions. The multi-objective, single-level optimization formulation of the problem provides a mutually consistent solution between the resulting O-D matrix and path/link flow pattern, which minimizes the difference between estimated and observed link flows. The model implementation into a real arterial sub-network demonstrates its ability to microscopically estimate trip demand with satisfactory accuracy and fast computing speeds which allow its usage in dynamic urban traffic operations. © 2008 Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Studies in Computational Intelligence	en
dc.identifier.doi	10.1007/978-3-540-69390-1_1	en
dc.identifier.volume	144	en
dc.identifier.spage	3	en
dc.identifier.epage	21	en