Fréchet Distance-Based Vehicle Selection and Satisfaction-Aware Vehicle Allocation for Demand-Responsive Shared Mobility: A Discrete Event Simulation Study

Demand-responsive transit (DRT) requires real-time vehicle assignment under dynamically arriving requests, where each decision may alter multi-stop routes and affect both onboard and newly arriving passengers. However, DRT simulations often face three key limitations: rapidly increasing computational complexity as fleet size and demand grow, insufficient integration of traffic congestion into routing decisions, and limited consideration of passenger-oriented service quality in final vehicle assignment. To address these issues, this study proposes an integrated DRT simulation incorporating three core algorithms: Fréchet Distance-based Candidate Vehicle Selection (FD-CVS), Congestion-Aware Path Planning (CA-PP), and Satisfaction-Aware Vehicle Assignment (SA-VA). FD-CVS reduces computational burden by filtering candidate vehicles based on route similarity. CA-PP extends conventional path planning by incorporating congestion-adjusted travel costs derived from public transportation data. SA-VA determines the final vehicle assignment by jointly evaluating passenger waiting time, in-vehicle travel time, and capacity constraints. The algorithms are implemented within a discrete-event simulation environment using real-world data. Experimental results demonstrate that FD-CVS significantly reduces execution time under high-demand conditions, while SA-VA improves passenger waiting time and acceptance rates. Overall, the proposed three-algorithm framework enables more realistic and computationally efficient DRT system evaluation.