A real-time traffic rescheduling approach for the metro system

Abstract

This paper addresses timetable disruptions in metro systems and their impact on passenger service quality. To mitigate these effects, the study introduces a time-space network-based traffic management approach with a two-layer network. The first layer focuses on timetable rescheduling, using an integer program for modeling and a Lagrangian relaxation-based heuristic for solution. The second layer addresses rolling stock duty rescheduling, employing a greedy heuristic for train set duties. The proposed approach is applied to a real-world problem presented in the INFORMS railway application competition (2022). Results demonstrate that, with topological vertex orders, the approach achieves schedule amendments within 3–6 minutes for various disruption scenarios. This rapid response highlights the method's efficacy and advantage in real-time applications, showcasing its potential for practical implementation in metro systems.

Keywords:

• Train timetable
• rolling stock duty
• rescheduling
• urban rail transit
• time-space network

Acknowledgments

The authors thank 2022 INFORMS RAS PSC Committee for their work and helpful comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was awarded the third place in the 2022 INFORMS RAS PSC, and supported in part by the National Natural Science Foundation of China (No. 72071059, 72188101, 71925001) and the Fundamental Research Funds for the Central Universities (No. JZ2023YQTD0073).