Integrated rescheduling of train timetables and rolling stock circulation for metro line disturbance management: a Q-learning-based approach

Abstract

Disturbance occurs inevitably on a metro line, resulting in train delays and low service quality. To improve the efficiency of disturbance management, this article investigates the integrated rescheduling problem of train timetable and rolling stock circulation. To balance service quality and operational cost, the considered problem is formulated as a multi-objective optimization model by capturing train operation processes as running arcs, dwell arcs and coming-out/back arcs based on the classical space–time network. For the purpose of improving computational efficiency, a Q-learning-based solution approach is proposed to derive a hierarchical policy for integrated rescheduling. The decisions on train timetable follow a lower-level policy that can be learned in the pre-training process, and the decisions on rolling stock circulation follow a higher-level policy that can be generated in the real-time planning process. Finally, two sets of numerical examples are carried out to evaluate the proposed method.

Keywords:

• Train timetable rescheduling
• rolling stock circulation
• disturbance management
• Q-learning

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The authors confirm that the data used to support the findings of this study are available within the article and its online supplemental data.

Notes

1 Scenario No. = (Location No. $-1$) × 12 + Duration No.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 52172322 and U22A2046]; the State Key Laboratory of Rail Traffic Control and Safety [grant number RCS2022ZZ003]; the Fundamental Research Funds for the Central Universities [grant number 2022JBQY001]; the Beijing Municipal Key Laboratory of Urban Rail Transit Automation and Control.