01660420

Component

https://doi.org/10.1177/0361198118796731

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.

18-04149

English

pp 81-90

2018-12

Transportation Research Record: Journal of the Transportation Research Board

Print

Figures (6) ; References (28) ; Tables (2)

Choice models; Logits; Route choice; Stated preferences; Time series analysis

Maryland Vehicle Stated Preference Survey

Highways; Operations and Traffic Management; Planning and Forecasting

TRIS, TRB, ATRI

Jan 8 2018 11:01AM

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