A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application.

Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of...

Full description

Bibliographic Details
Main Authors: Yongsheng Zhang, Enjian Yao, Heng Wei, Kangning Zheng
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5462412?pdf=render
id doaj-725d9f799032437ca261b77b1aa318ab
record_format Article
spelling doaj-725d9f799032437ca261b77b1aa318ab2020-11-24T21:48:06ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01126e017878910.1371/journal.pone.0178789A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application.Yongsheng ZhangEnjian YaoHeng WeiKangning ZhengConsidering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of a constrained multinomial probit (CMNP) route choice model in the metro network are carried out in this paper. The utility function is formulated as three components: the compensatory component is a function of influencing factors; the non-compensatory component measures the impacts of routes set on utility; following a multivariate normal distribution, the covariance of error component is structured into three parts, representing the correlation among routes, the transfer variance of route, and the unobserved variance respectively. Considering multidimensional integrals of the multivariate normal probability density function, the CMNP model is rewritten as Hierarchical Bayes formula and M-H sampling algorithm based Monte Carlo Markov Chain approach is constructed to estimate all parameters. Based on Guangzhou Metro data, reliable estimation results are gained. Furthermore, the proposed CMNP model also shows a good forecasting performance for the route choice probabilities calculation and a good application performance for transfer flow volume prediction.http://europepmc.org/articles/PMC5462412?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Yongsheng Zhang
Enjian Yao
Heng Wei
Kangning Zheng
spellingShingle Yongsheng Zhang
Enjian Yao
Heng Wei
Kangning Zheng
A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application.
PLoS ONE
author_facet Yongsheng Zhang
Enjian Yao
Heng Wei
Kangning Zheng
author_sort Yongsheng Zhang
title A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application.
title_short A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application.
title_full A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application.
title_fullStr A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application.
title_full_unstemmed A constrained multinomial Probit route choice model in the metro network: Formulation, estimation and application.
title_sort constrained multinomial probit route choice model in the metro network: formulation, estimation and application.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2017-01-01
description Considering that metro network expansion brings us with more alternative routes, it is attractive to integrate the impacts of routes set and the interdependency among alternative routes on route choice probability into route choice modeling. Therefore, the formulation, estimation and application of a constrained multinomial probit (CMNP) route choice model in the metro network are carried out in this paper. The utility function is formulated as three components: the compensatory component is a function of influencing factors; the non-compensatory component measures the impacts of routes set on utility; following a multivariate normal distribution, the covariance of error component is structured into three parts, representing the correlation among routes, the transfer variance of route, and the unobserved variance respectively. Considering multidimensional integrals of the multivariate normal probability density function, the CMNP model is rewritten as Hierarchical Bayes formula and M-H sampling algorithm based Monte Carlo Markov Chain approach is constructed to estimate all parameters. Based on Guangzhou Metro data, reliable estimation results are gained. Furthermore, the proposed CMNP model also shows a good forecasting performance for the route choice probabilities calculation and a good application performance for transfer flow volume prediction.
url http://europepmc.org/articles/PMC5462412?pdf=render
work_keys_str_mv AT yongshengzhang aconstrainedmultinomialprobitroutechoicemodelinthemetronetworkformulationestimationandapplication
AT enjianyao aconstrainedmultinomialprobitroutechoicemodelinthemetronetworkformulationestimationandapplication
AT hengwei aconstrainedmultinomialprobitroutechoicemodelinthemetronetworkformulationestimationandapplication
AT kangningzheng aconstrainedmultinomialprobitroutechoicemodelinthemetronetworkformulationestimationandapplication
AT yongshengzhang constrainedmultinomialprobitroutechoicemodelinthemetronetworkformulationestimationandapplication
AT enjianyao constrainedmultinomialprobitroutechoicemodelinthemetronetworkformulationestimationandapplication
AT hengwei constrainedmultinomialprobitroutechoicemodelinthemetronetworkformulationestimationandapplication
AT kangningzheng constrainedmultinomialprobitroutechoicemodelinthemetronetworkformulationestimationandapplication
_version_ 1725893413568512000