Predicting mode choice through multivariate recursive partitioning

Karlaftis, MG

dc.contributor.author	Karlaftis, MG	en
dc.date.accessioned	2014-03-01T01:21:14Z
dc.date.available	2014-03-01T01:21:14Z
dc.date.issued	2004	en
dc.identifier.issn	0733-947X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16156
dc.subject	Predictions	en
dc.subject	Transportation models	en
dc.subject	Transportation planning	en
dc.subject	Travel demand	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Forecasting	en
dc.subject.other	Mathematical models	en
dc.subject.other	Mathematical techniques	en
dc.subject.other	Multivariate recursive partitioning	en
dc.subject.other	Transportation	en
dc.subject.other	modeling	en
dc.subject.other	prediction	en
dc.subject.other	transportation mode	en
dc.subject.other	travel behavior	en
dc.subject.other	travel demand	en
dc.title	Predicting mode choice through multivariate recursive partitioning	en
heal.type	journalArticle	en
heal.identifier.primary	10.1061/(ASCE)0733-947X(2004)130:2(245)	en
heal.identifier.secondary	http://dx.doi.org/10.1061/(ASCE)0733-947X(2004)130:2(245)	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	Understanding and predicting individual mode choice decisions can help address issues ranging from forecasting demand for new modes of transport to understanding the underlying traveler behavior and characteristics. Early research in mode choice modeling revolved, almost exclusively, around the family of logit models. But a number of researchers have recently argued that these models place restrictions on their parameters that compromise their performance and have thus experimented with a number of newly developed, flexible mathematical techniques. The present paper extends prior research by developing a methodology for predicting individual mode choice based on a nonparametric classification methodology that imposes very few constraining assumptions in yielding mode choice predictions. Preliminary results, using data from three vastly different international settings, are promising, especially when considering that the models are successful while using only a limited number of independent variables to achieve these predictions. © ASCE / MARCH/APRIL 2004.	en
heal.publisher	ASCE-AMER SOC CIVIL ENGINEERS	en
heal.journalName	Journal of Transportation Engineering	en
dc.identifier.doi	10.1061/(ASCE)0733-947X(2004)130:2(245)	en
dc.identifier.isi	ISI:000220045900011	en
dc.identifier.volume	130	en
dc.identifier.issue	2	en
dc.identifier.spage	245	en
dc.identifier.epage	250	en