Travel Choice Analysis under Metro Emergency Context: Utility? Regret? Or Both?

Abstract

With the continuous expansion of the network scale and increasing of passengers, metro emergencies such as operational equipment failure are happening more frequently. Due to the narrow space and crowds of people, metro emergencies always have more of an impact than road traffic emergencies. In order to adopt appropriate measures to ensure passenger safety and avoid risks, we need to get a better understanding of passengers’ travel choice behaviors under emergencies. Most of the existing research studies related to travel choice behaviors took the random utility maximization (RUM) principle for granted, but failed to realize the potential of different decision-making processes and changes to the decision-making environment. In this research, we aim to analyze metro passengers’ travel choice behaviors under metro network emergency contexts. Based on the data collected from a survey about travel choices under metro emergencies in the Guangzhou Metro, we compared the performances of models that follow the RUM and random regret minimization (RRM) principles, and established a hybrid RUM-RRM model as well as a nested logit model following RRM (NL-RRM) to estimate the effects of various factors on passengers’ travel choice behaviors. Comparisons illustrate that the hybrid model and NL-RRM model can improve model fit, and the combination of RUM and RRM outperforms either of them respectively.

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

“…For Scenario I (distance <6 km), the log-likelihoods of the five individual components show RUM model performs slightly better than regret-based models, which is different from the results obtained by Chorus ( Chorus, 2014 ), An et al (2015) , Wang et al (2018) and Anowar et al (2019) . However, in line with the results reported in previous studies, the treatment of log-likelihoods and parameters for RRM and GRRM model is preferred to the pure RUM treatment suggesting that regret-based models fit the database of Scenario II and III better than linear-in-parameters RUM models and the difference is small.…”

“…It is worth mentioning that the ability to capture the loss aversion psychology of individuals attracts more scholars attention to apply RRM model in the context of emergency ( An et al, 2015 ; Wang et al, 2018 ) and they have already shown the better performance to account for evacuees' regret aversion psychology. As the outbreak of COVID-19 is a serious global public health concern, the authors predicted that the threat of epidemic might cause great trepidation and alarm and bring out the regret aversion psychology to make decisions.…”

Do Urban Subway Openings Reduce PM2.5 Concentrations? Evidence from China

“…Two ways were proposed including public control and restriction, as well as public investment in low-emission transport infrastructure. Some literature also focused on the transport behavior, which concluded that major transport policy that could reduce passenger travel levels would be most effective in transport emission cuts [22,[39][40][41].…”

Impact Estimation of Unplanned Urban Rail Disruptions on Public Transport Passengers: A Multi-Agent Based Simulation Approach

“…The travel adjustment choice of the affected passengers at the micro level is finally reflected in the redistribution of passenger flow on the transportation network at the macro level. Thus, some studies were dedicated to understanding the mechanism of passengers’ behaviors and constructing passenger reassignment models under unplanned rail disruptions [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Generally, these works were conducted by means of a passenger flow survey.…”

“…Considering uncertain disruption duration, Li et al [ 14 ] developed a nested logit model to explore metro passengers’ travel plan choice behavior under unplanned service disruptions. Wang et al [ 15 ] established a nested logit model following random regret minimization principles to estimate passenger travel choice behaviors under metro emergency context. Dai et al [ 16 ] also proposed a nested logit model for the decision-making of affected passengers in a metro emergency evacuation.…”