Dynamic Minimum Service Level of Demand–Responsive Transit: A Prospect Theory Approach

Abstract

Demand–responsive transit (DRT) provides flexible, user-centric services and is gaining attention as a solution to modern transportation challenges. Establishing a minimum service level is crucial for its effectiveness, yet existing methods rely on supplier-centric indicators that fail to reflect user psychology and the flexible nature of DRT. To address this, this study applied a prospect theory from behavioral economics and used logistic regression analysis of stated preference survey data to determine minimum service levels based on user perceptions. To account for regional variations, we classified user groups based on primary transportation mode, travel purpose, and age, proposing dynamic minimum service levels tailored to each group. Additionally, using the maximum likelihood estimation method, we estimated value function parameters for the prospect theory, allowing us to analyze users’ loss aversion and sensitivity to DRT services. The findings indicated that users would accept higher fares for DRT than for conventional public transportation, provided it offers shorter travel times. Sensitivity to service levels varied across user groups, highlighting the need for differentiated policies. This study provides insights to optimize DRT operations, improve user satisfaction, and guide policies that reflect regional and demographic characteristics, enhancing the efficiency and effectiveness of DRT services.