Traffic flow evolution effects to nitrogen dioxides predictability in large metropolitan areas

Vlahogianni, EI; Golias, JC; Ziomas, IC

dc.contributor.author	Vlahogianni, EI	en
dc.contributor.author	Golias, JC	en
dc.contributor.author	Ziomas, IC	en
dc.date.accessioned	2014-03-01T01:37:30Z
dc.date.available	2014-03-01T01:37:30Z
dc.date.issued	2011	en
dc.identifier.issn	1361-9209	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21525
dc.subject	Modular prediction genetic algorithms	en
dc.subject	Nitrogen dioxide prediction	en
dc.subject	Temporal neural networks	en
dc.subject.classification	Environmental Studies	en
dc.subject.classification	Transportation	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Metropolitan area	en
dc.subject.other	Modular neural networks	en
dc.subject.other	Modular prediction genetic algorithms	en
dc.subject.other	Nitrogen dioxide prediction	en
dc.subject.other	Nitrogen dioxides	en
dc.subject.other	Prediction horizon	en
dc.subject.other	Static neural networks	en
dc.subject.other	Temporal neural networks	en
dc.subject.other	Time windows	en
dc.subject.other	Traffic flow	en
dc.subject.other	Traffic information	en
dc.subject.other	Traffic volumes	en
dc.subject.other	Travel speed	en
dc.subject.other	Urban freeways	en
dc.subject.other	Air pollution	en
dc.subject.other	Biology	en
dc.subject.other	Chemical sensors	en
dc.subject.other	Forecasting	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Nitrogen	en
dc.subject.other	Nitrogen oxides	en
dc.subject.other	Ozone	en
dc.subject.other	Traffic control	en
dc.subject.other	Traffic surveys	en
dc.subject.other	Neural networks	en
dc.subject.other	artificial neural network	en
dc.subject.other	genetic algorithm	en
dc.subject.other	metropolitan area	en
dc.subject.other	nitrogen dioxide	en
dc.subject.other	prediction	en
dc.subject.other	traffic congestion	en
dc.subject.other	travel behavior	en
dc.subject.other	urban transport	en
dc.title	Traffic flow evolution effects to nitrogen dioxides predictability in large metropolitan areas	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.trd.2011.01.001	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.trd.2011.01.001	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	A genetically-optimized modular neural network is used to predict the temporal of nitrogen dioxides in a highly congested urban freeway by integrating, in a single prediction shell, information on past values of nitrogen dioxide and ozone, as well as traffic volume, travel speed and occupancy. Results indicate that the approach is more accurate for one and multiple steps ahead predictions when compared to a simple static neural network. They also indicate that the integration of traffic information in the process of prediction improves to some extent the predictability of nitrogen dioxides evolution. It is also shown that the look-back time window for pollutants-related data increases with relation to the increase of the prediction horizon. (C) 2011 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Transportation Research Part D: Transport and Environment	en
dc.identifier.doi	10.1016/j.trd.2011.01.001	en
dc.identifier.isi	ISI:000289181300001	en
dc.identifier.volume	16	en
dc.identifier.issue	4	en
dc.identifier.spage	273	en
dc.identifier.epage	280	en