Robust optimization for adaptive bus service scheduling with adversarial reinforcement learning under demand uncertainties

Authors

• Guang-yu Li
• Andy H.F. Chow
• Cheng-shuo Ying

Document Type

Journal Article

Publication Date

2025

Subject Area

place - asia, place - urban, mode - bus, operations - scheduling, technology - intelligent transport systems

Keywords

Adaptive transit scheduling, Markov decision process, Attacker–defender game, Adversarial reinforcement learning, Robust optimization

Abstract

This paper presents a robust optimization framework for adaptive bus service scheduling with use of adversarial deep reinforcement learning subject to demand uncertainties. The optimization problem is formulated as an attacker–defender game theoretic Markov decision process in which the attacker aims to cause disturbances by imposing demand perturbations to the bus service operations. The defender on the other hand aims to schedule and adjust the bus services that can minimize the passengers’ waiting times and bus operator’s costs given the demand variations driven by the attacker. A reinforcement learning approach is adopted for solving the Markov decision game framework in which the state and decision spaces encountered by both attacker and defender are to be approximated by artificial neural network (ANN) surrogates that are to be trained with double deep Q network (DDQN) algorithm. The proposed framework is tested using a real-world scenario adopted from Hong Kong bus services. Results indicate that the proposed computational framework can deliver efficient and robust performance with presence of stochastic demand variations compared with other existing methods. This study contributes to development of robust real-time bus services through advanced optimization techniques.

Rights

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

Comments

Transportation Research Part C Home Page:

http://www.sciencedirect.com/science/journal/0968090X

Recommended Citation

Li, G. Y., Chow, A. H., & Ying, C. S. (2025). Robust optimization for adaptive bus service scheduling with adversarial reinforcement learning under demand uncertainties. Transportation Research Part C: Emerging Technologies, 178, 105222.