Share:


A cumulative prospect theory approach to car owner mode choice behaviour prediction

    Shi An Affiliation
    ; Xiaowei Hu Affiliation
    ; Jian Wang Affiliation

Abstract

The uncertain transportation environment makes travel’s mode choice decision-making behaviour become a complex and alterable process. Based on the cumulative prospect theory, this paper analysed the long-standing use of utility theory for the travel’s mode choice behaviour research. Car owner’s generalized cost includes the transport fare, travel time cost and penalty cost (early or delay); cumulative prospect theory was applied to describe the uncertain and risky prospect of car owner under congestion pricing policy. Through analysing two kinds of car owner’s generalized subjective perception costs on the four different transportation modes, including bus, subway, taxi and private car; we calculated the mode choice’s prospect value before and after the implementation of congestion pricing, and compared the difference of numerical example between cumulative prospect theory and expected utility theory. The results indicated that after the implementation of congestion pricing policy, the middle-level income car owner would prefer to choose taxi. Based on a state preference survey on travel’s mode choice behaviour, the survey results further validated our analysis. This paper for the first time adopted cumulative prospect theory to analyse travel’s mode choice behaviour after the implementation of congestion pricing policy, which can better explain car owner’s mode choice decisionmaking process under uncertain and risk condition. This study also can be helpful to many cities that wish to establish and implement the congestion pricing policy in practice.


First Published Online: 16 Dec 2014

Keyword : travel behaviour, cumulative prospect theory, utility theory, mode choice, congestion pricing, stated preference survey

How to Cite
An, S., Hu, X., & Wang, J. (2014). A cumulative prospect theory approach to car owner mode choice behaviour prediction. Transport, 29(4), 386–394. https://doi.org/10.3846/16484142.2014.983161
Published in Issue
Dec 31, 2014
Abstract Views
793
PDF Downloads
606
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.