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Title:

Comparison of Fully Probabilistic and Partially Probabilistic Choice Set Models for Mode Choice

Accession Number:

01849822

Record Type:

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Order URL: http://worldcat.org/issn/03611981

Abstract:

Contemporary models consider choice sets to be either fully deterministic or fully probabilistic. Deterministic choice set models do not account for stochasticity in the choice set formation, whereas probabilistic choice set models fail to recognize that exclusion and inclusion can be deterministic for some alternatives and individuals and yet random for others. A more general scenario is, therefore, where some alternatives are deterministically included or excluded and others probabilistically included. This paper proposes a richer framework that combines the features of both deterministic and probabilistic choice set models and explicitly allows an alternative to be deterministically included, deterministically excluded, or probabilistically considered in the choice set. This framework is better than the conventional models in four aspects: (a) the factors influencing consideration type are explicitly and parametrically analyzed instead of assumption as 0 or 1; (b) the specification can disentangle factors that affect the inclusion outcome from the type of consideration; and (c) the specification also permits differential sensitivity to factors in conditional choice probability among those who consider an alternative deterministically versus probabilistically. The partially probabilistic choice set model, a special case of the proposed generalized framework, developed using empirical data collected from working commuters in Chennai city, is benchmarked against the fully probabilistic choice set models. The results show that the former had improved goodness-of-fit, realistic consideration probability estimates, and better predictability of mode shares than the latter. Relevant policies have been evaluated by identifying the appropriate target segments at both the consideration and choice stages using the proposed model.

Supplemental Notes:

Parthan Kunhikrishnan https://orcid.org/0000-0002-0934-880X© National Academy of Sciences: Transportation Research Board 2022.

Language:

English

Authors:

Kunhikrishnan, Parthan

ORCID 0000-0002-0934-880X

Srinivasan, Karthik K

Pagination:

pp 1213-1227

Publication Date:

2023-1

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Volume: 2677
Issue Number: 1
Publisher: Sage Publications, Incorporated
ISSN: 0361-1981
EISSN: 2169-4052
Serial URL: http://journals.sagepub.com/home/trr

Media Type:

Web

Features:

References (23)

Geographic Terms:

Subject Areas:

Planning and Forecasting; Transportation (General)

Files:

TRIS, TRB, ATRI

Created Date:

Jun 26 2022 3:01PM