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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. [ABSTRACT FROM AUTHOR]