Cognitive Process Model of Individual Choice Behaviour Incorporating Principles of Bounded Rationality and Heterogeneous Decision Heuristics

Exploring the impact of COVID-19 on individual's travel mode choice in China

Travel activities and travel behaviors have been greatly affected by the outbreak of Covid-19. Facing the change of individuals' travel choices, policymakers have to make an appropriate response to mitigate negative consequences. This paper aims to explore how the COVID-19 would impact travel mode choice and the intention of car purchase. The data was collected from a large-scale survey conducted in June 2020 after the highest point. Random utility maximization (RUM), random regret minimization (RRM) and generalized regret minimization (GRRM) are employed to examine the effects of various factors on mode choice behaviors. The estimation results reveal that regret aversion psychology doesn't have a dominant proportion of decision choices, even if the congested condition of the mass mobility plays a significant role in the consideration of decision-making. Combined with the statistical results from the official departments, we concluded that public transport displays a great propensity on the long trip, and meanwhile, the industry of ride-hailing services has shocked sharply. In terms of the intention of traffic tool purchase, carless people prefer to buy electric two-wheel vehicles rather than automobiles. The research findings and the contribution to policy implications give assistance to authority in understanding citizens' travel mode preferences under the impact of COVID-19.

Regret Minimization or Utility Maximization: It Depends on the Attribute

In this study we show how the coexistence of different decision rules can be accommodated in discrete choice models. Specifically, in this paper we present a generic hybrid model specification that allows for some attributes being processed using conventional linear-additive utility-maximization-based rules, while others are being processed using regret-minimization-based rules. We show that on two revealed and stated choice datasets particular specifications of hybrid models, containing both regret-based and utility-based attribute decision rules, outperform—in terms of model fit and out-of-sample predictive ability—choice models where all attributes are assumed to be processed by means of one and the same decision rule. However, in our data differences between models are very small. Implications, in terms of marginal willingness-to-pay measures (WtP), are derived for the different hybrid model specifications and applied in the context of the two datasets. It is found that in the context of our data hybrid WtP measures differ substantially from conventional utility-based WtP measures, and that the hybrid WtP specifications allow for a richer (choice-set-specific) interpretation of the trade-offs that people make.