Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2018

Abstract

This paper considers a sequential discrete choice problem in a time domain, formulated and solved as a route choice problem in a space domain. Starting from a dynamic specification of time-series discrete choices, we show how it is transferrable to link-based route choices that can be formulated by a finite path choice multinomial logit model. This study establishes that modeling sequential choices over time and in space are equivalent as long as the utility of the choice sequence is additive over the decision steps, the link-specific attributes are deterministic, and the decision process is Markovian. We employ the recursive logit model proposed in the context of route choice in a network, and apply it to estimate time-series vehicle type choice based on Maryland Vehicle Stated Preference Survey data. The model has been efficiently estimated by a dynamic programming approach; the values of estimated coefficients provide important patterns on vehicle type preferences. Compared with a naive model based on sequential multinomial logit choices which are independent over time and a dynamic discrete choice model which considers agent’s future expectations, the smaller root mean square error of recursive logit model indicates that it has a better performance in estimating sequential choices over time. We also compare the predictive powers and find that the proposed model outperforms the basic approach and the dynamic approach.

Discipline

Theory and Algorithms | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Transportation Research Record

Volume

2672

Issue

49

First Page

81

Last Page

90

ISSN

0361-1981

Identifier

10.1177/0361198118796731

Publisher

SAGE Publications (UK and US)

Embargo Period

3-28-2021

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1177/0361198118796731

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