A spatial rank-ordered probit model with an application to travel mode choice. (January 2022)

• Record Type: Journal Article
• Authors: Mondal, Aupal; Bhat, Chandra R.
• Is Part Of: Transportation research. Volume 155(2022)
• Page Start: 374
• Page End: 393
• Publication Date: 2022-01
• Subjects: Ranked data analysis -- Probit models -- Spatial econometrics -- Travel mode choice -- Autonomous vehicles; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2021.12.008
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: A spatial rank-ordered probit model is proposed for the first time. A simulation study shows good performance of the proposed estimator. The simulator study indicates serious bias when ignoring spatial effects. A travel mode choice analysis application is undertaken. Abstract: Ranking data provide important additional information related to valuation because of the implied preference sequence among all alternatives, rather than just the top choice preference. This additional information from a preference ranking can be exploited to achieve a certain desired precision in choice model estimation with a much smaller sample size, making ranked data surveys much more cost-effective than first-choice surveys. In this paper, we propose a spatial rank-ordered probit (SROP) model that accommodates both spatial lag effects as well as spatial drift effects. To our knowledge, this is the first such formulation and application of an SROP model in the econometric and transportation literature. An application of the proposed model is demonstrated in a travel mode choice ranking experiment among seven alternatives, including autonomous vehicle (AV) private ride-hailing and AV pooled ride-hailing.