Scheduling Optimisation of Demand-Responsive Transit Considering Passenger Order Cancellation Behavior

Event Details

Professor Xumei Chen of Beijing Jiaotong University will present a seminar to the Management Science Department

Abstract: Demand-responsive transit (DRT) offers a flexible alternative to conventional bus services by adapting routes and schedules to passenger demand. However, passenger cancellations create operational uncertainty, potentially wasting vehicle resources and disrupting service for other passengers. Addressing these challenges requires understanding how waiting experiences, pricing policies and competing transport options influence cancellation decisions, and incorporating these interactions into service planning.

In this study, we develop an integrated framework that connects passenger cancellation behaviour, flexible fare strategies and vehicle scheduling. We use prospect theory to represent passengers’ perceptions of waiting time deviations and design corresponding fare adjustments alongside time-dependent cancellation charges. A cancellation probability model captures passengers’ decisions to switch to taxis while waiting for DRT services. These behavioural components inform a scheduling framework that combines advance planning with rolling updates to accommodate new requests and cancellations. An enhanced spatiotemporal clustering method identifies and updates passenger meeting points, while an adaptive large neighbourhood search algorithm optimises vehicle routes and schedules.

Computational experiments on the Sioux Falls network and a case study in Beijing’s Tiantongyuan area demonstrate the framework’s potential to reduce system net costs and vehicle requirements while serving more passengers than a model that ignores cancellations. Further analyses examine how passenger perceptions, taxi availability and cancellation charges affect cancellation behaviour and system performance. The findings highlight the value of incorporating passenger behaviour into operational decisions and provide practical insights into pricing and scheduling strategies for more efficient and reliable DRT services.