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. 2023 Mar 2:1–14. Online ahead of print. doi: 10.1007/s12204-023-2588-9

Random Regret Minimization Model of Carpool Travel Choice for Urban Residents Considering Perceived Heterogeneity and Psychological Distance

考虑感知异质性和心理距离的随机后悔最小化城市居民合乘出行选择模型

Qiang Xiao 1,3,✉, Ruichun He 2, Ziyi Wang 1
PMCID: PMC9981256  PMID: 37359452

Abstract

Carpooling is a sustainable, economical, and environmentally friendly solution to reduce air pollution and ease traffic congestion in urban areas. However, existing regret theories lack consideration of the heterogeneity of attribute perception in different ways and the psychological factors that affect regret, so they cannot accurately portray urban residents’ carpool travel decisions and cannot provide a correct explanation of the actual carpool choice behavior. In this paper, based on the analysis of classical random regret minimization models and random regret minimization models considering heterogeneity, the concept of psychological distance is introduced to address shortcomings of the existing models and construct an improved random regret minimization model considering heterogeneity and psychological distance. The results show that the fit and explanatory effect of the improved model proposed in this paper is better than that of the other two models. The psychological distance of travel residents during the Corona Virus Disease 2019 (COVID-19) affects the anticipated regret value and the willingness to carpool. The model can better describe the carpool travel choice mechanism of travelers and effectively explain the carpool travel choice behavior of travelers.

Key words: carpool travel behavior, random regret minimization theory, anticipated regret value, perceived heterogeneity, psychological distance

Footnotes

Foundation item

the National Natural Science Foundation of China (No. 52062026), the Educational Commission of Gansu Province of China (No. 2019A-041), and the Double-First Class Major Research Programs of Educational Department of Gansu Province (No. GSSYLXM-04)

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