01515616

Component

In order to capture the dependency between socio-demographic characteristics of individuals and their daily activities as well as the correlation among different activity types, the authors fit a multivariate probit (MVP) model to individual activity participation diary data. Due to the correlation among different activity types, analytical solutions under maximizing the likelihood function are intractable; in this paper, model parameters are estimated using parameter expansion and reparameterizaton with the Metropolis Hastings algorithm. The algorithm is based on Markov Chain Monte Carlo (MCMC) sampling techniques, with the assumption of normal and inverse Wishart distributions for regression parameters and unrestricted covariance matrix. Two sets of explanatory variables at individual and household levels are used in the study and the results indicate the influence of various socio-demographic variables on activity types that individuals select. A comparison between a multivariate probit model and an independent model, estimated on San Diego and Orange County travel data, indicates that higher accuracy is achieved using the correlated model in both in-sample and out-of sample data.

This paper was sponsored by TRB committee ADB40 Transportation Demand Forecasting. Alternate title: Multivariate Probit Model of Activity Participation Behavior.

01503729

14-1474

English

18p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

Figures; References; Tables

Activity choices; Households; Markov chains; Monte Carlo method; Multivariate analysis; Probits; Travel diaries

Sociodemographic characteristics

Orange County (California); San Diego (California)

Highways; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2014 Paper #14-1474

TRIS, TRB, ATRI

Jan 27 2014 2:33PM