Real-time scheduling optimization for autonomous public transport vehicles to meet booking demands

Authors

• Zhichao Cao
• Avishai (Avi) Ceder
• Silin Zhang

Document Type

Journal Article

Publication Date

2025

Subject Area

place - urban, place - australasia, mode - bus, mode - demand responsive transit, infrastructure - fleet management, planning - service improvement, planning - travel demand management, planning - methods, technology - automatic vehicle monitoring, technology - intelligent transport systems

Keywords

Autonomous public transport vehicle, Advanced booking, Deficit function, Fleet size, Timetabling

Abstract

The booking service, a key feature of autonomous public transport vehicle (APTV) systems, has been designed to introduce a new, real-time, on-demand, and reliable element to service improvement, similar to ride-hailing. However, the current APTV system has yet to fully realize the potential of a smart public transport service in optimizing the balance between supply and demand. This study proposes a real-time, multi-objective programming model that aims to minimize three key factors: passenger waiting times, timetable deviations, and fleet size. Recognized as an NP-hard problem, the model is linearized to reduce computational complexity, with real-time demands tracked through a rolling horizon method. A predict-then-optimize approach is introduced to enable timely responses to new bookings. A customized two-phase algorithm incorporating three enhancements − valid cuts, Monte Carlo simulation, and neighborhood and local search − significantly improves solution efficiency. A case study in Auckland, New Zealand, evaluates the proposed approach. The findings reveal significant improvements in booking service performance, with two scenarios achieving a 35 % and 27 % reduction in passenger waiting time and a 13 % and 12 % decrease in fleet size compared to the current conventional bus line. These results were attained with minimal deviations from the original schedule, validating the effectiveness of the developed methodology.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part E Home Page:

http://www.sciencedirect.com/science/journal/13665545

Recommended Citation

Cao, Z., Ceder, A. A., & Zhang, S. (2025). Real-time scheduling optimization for autonomous public transport vehicles to meet booking demands. Transportation Research Part E: Logistics and Transportation Review, 200, 104202.