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Title:

Modeling Co-dependent Choice of Workplace, Residence and Commuting Mode Using an Error Component Mixed Logit Model

Accession Number:

01659536

Record Type:

Component

Abstract:

This paper develops an error component mixed logit model to analyze the multi-dimensional choice of residential, work and transportation mode choice. Although several studies based on life-trajectory theory considered different life-domains, most of this research used retrospective data and only considered two dimensions. In contributing to this emerging field of research, in this project the authors designed an integrated D-optimal pivoted stated choice experiment considering the multi-dimensional choice of job, residence and transportation mode for the journey to work. The results of the estimated error component mixed logit model with panel effects indicated that most selected attributes of the residential environment, job profile and transportation mode are significantly related to multidimensional choices. Moreover, the estimation of various sources of unobserved heterogeneity signaled significant unobserved heterogeneity in selected taste parameters, and choice dependent heteroscedasticity in error component variance.

Supplemental Notes:

This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting. Alternate title: Modeling Codependent Choice of Workplace, Residence, and Commuting Mode Using an Error Component Mixed Logit Model

Report/Paper Numbers:

18-01327

Language:

English

Authors:

Guo, Jia
Feng, Tao
Timmermans, Harry

Pagination:

15p

Publication Date:

2018

Conference:

Transportation Research Board 97th Annual Meeting

Location: Washington DC, United States
Date: 2018-1-7 to 2018-1-11
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

References; Tables

Subject Areas:

Passenger Transportation; Planning and Forecasting

Source Data:

Transportation Research Board Annual Meeting 2018 Paper #18-01327

Files:

TRIS, TRB, ATRI

Created Date:

Jan 8 2018 10:20AM