Bayesian Multinomial Logit Theory and Route Choice Example

Washington, S; Congdon, P; Karlaftis, MG; Mannering, FL

dc.contributor.author	Washington, S	en
dc.contributor.author	Congdon, P	en
dc.contributor.author	Karlaftis, MG	en
dc.contributor.author	Mannering, FL	en
dc.date.accessioned	2014-03-01T01:58:51Z
dc.date.available	2014-03-01T01:58:51Z
dc.date.issued	2009	en
dc.identifier.issn	0361-1981	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28756
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Transportation	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	CHAIN MONTE-CARLO	en
dc.subject.other	TRAVEL MODE CHOICE	en
dc.subject.other	CONVERGENCE	en
dc.subject.other	LIKELIHOOD	en
dc.title	Bayesian Multinomial Logit Theory and Route Choice Example	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Statisticians along with other scientists have made significant computational advances that enable the estimation of formerly complex statistical models. The Bayesian inference framework combined with Markov chain Monte Carlo estimation methods such as the Gibbs sampler enable the estimation of discrete choice models such as the multinomial logit (MNL) model. MNL models are frequently applied in transportation research to model choice outcomes such as mode, destination, or route choices or to model categorical outcomes such as crash outcomes. Recent developments allow for the modification of the potentially limiting assumptions of MNL such as the independence from irrelevant alternatives (IIA) property. However, relatively little transportation-related research has focused on Bayesian MNL models, the tractability of which is of great value to researchers and practitioners alike. This paper addresses MNL model specification issues in the Bayesian framework, such as the value of including prior information on parameters, allowing for nonlinear covariate effects, and extensions to random parameter models, so changing the usual limiting HA assumption. This paper also provides an example that demonstrates, using route-choice data, the considerable potential of the Bayesian MNL approach with many transportation applications. This paper then concludes with a discussion of the pros and cons of this Bayesian approach and identifies when its application is worthwhile.	en
heal.publisher	NATL ACAD SCIENCES	en
heal.journalName	TRANSPORTATION RESEARCH RECORD	en
dc.identifier.isi	ISI:000274489300004	en
dc.identifier.issue	2136	en
dc.identifier.spage	28	en
dc.identifier.epage	36	en