Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory

Kexin Geng, Yacan Wang, Elisabetta Cherchi and Pablo Guarda

Transportation Research Part A: Policy and Practice, 2023, vol. 168, issue C

Abstract: An often-overlooked problem in the evaluation and prediction of congestion charge policies is commuters’ bounded rationality. Although some studies have sought to account for this using cumulative prospect theory (CPT), the specific behavioral parameters that reflect travelers’ decision-making process in response to congestion charge scenarios are based on assumptions and lack empirical evidence. This paper aims to provide empirical evidence to define the shape parameters in CPT—while accounting for systematic heterogeneity due to commuters’ characteristics—in order to build more realistic behavioral models for car commuters’ departure time choice behavior under congestion charge scenarios. A stated preference (SP) experiment with four time-based congestion charge scenarios is designed to obtain commuters’ departure time choices when facing uncertain travel conditions. A genetic algorithm (GA) is used to estimate the CPT coefficients that reflect car commuters’ cognitive biases under the congestion charge. The results suggest that commuters’ departure time choice under the congestion charge policy is consistent with the assumption of CPT. We find evidence of risk-averse and risk-taking behavior, loss aversion, and large distortion in probability weighting, and individuals’ personal and commuting characteristics had heterogeneous effects on CPT coefficients. The results shed light on travelers’ behavioral responses to congestion charge schemes and provide an important empirical reference.

Keywords: Cumulative Prospect Theory; Congestion Charge; Departure Time Choice; Genetic Algorithm (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.tra.2022.103564

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