Optimization of system resilience in urban rail systems: Train rescheduling considering congestions of stations

• Published in: Computers & industrial engineering 2023-11, Vol.185, p.109657, Article 109657
• Main Authors: Li, Zhixian, Yin, Jiateng, Chai, Simin, Tang, Tao, Yang, Lixing
• Format: Article
• Language: English
• Subjects: Congestion of stations; Resilience improvement; Train rescheduling; Two-phase iterative optimization
• ISSN: 0360-8352
• DOI: 10.1016/j.cie.2023.109657

In urban rail systems, unexpected events, due to equipment breakdown, extreme weather, etc., frequently cause the delay of trains and overcrowding of platforms, significantly affecting the service quality to passengers. Therefore, improving system resilience to recover to normal conditions more quickly has become a critical problem in the management of urban rail systems. In contrast to most previous studies that focused only on train rescheduling, our paper proposes optimizing the resilience of urban rail systems by considering the influence of delayed trains and overcrowded passenger flows. Specifically, we define a resilience-oriented quantitative evaluation index. Using the evaluation index as the objective function, we construct a mixed-integer nonlinear programming model. In our formulation, we consider a practical case in which the boarding and alighting of passengers at overcrowded stations may further delay trains. To solve the model, we develop a two-phase iterative optimization approach, in which phase 1 focuses on the adjustment of trains to enhance system resilience, and phase 2 evaluates the impact of passengers on the adjusted train schedule. Through the comparison of three different rescheduling methods, we prove the validity of the derived inequalities and our approach balances the computational time and system performance. Case studies are conducted on the Beijing Metro Line 1 to verify the effectiveness of the proposed approach. The results demonstrate that system resilience can be improved by as much as 39.7% using our optimization approach. •An optimization approach of system resilience in urban rail systems is developed.•Over-crowded platforms extend the train delays.•The two-phase iterative approach considers the time-dependent congestion level.•A rescheduling timetable is generated for resilience improvement.