Sequence-preserving disturbance mitigation strategies for scheduled mobility systems

Operational disturbances in scheduled mobility systems manifest primarily as delays, where events occur later than planned and the resulting lateness can cascade through schedules. While rescheduling theory often explores re-sequencing strategies to recover from delays, real-world scheduled mobility systems, from metros to Connected and Automated Vehicles (CAV), often strictly maintain service order to adhere to infrastructure constraints, preserve network connectivity, and ensure real-time computational feasibility. Motivated by these operational realities, this paper develops a unified sequence-preserving event graph framework. On this event graph, the basic mitigation strategy (BMS) computes the componentwise least feasible timing plan, while the hybrid mitigation strategy (HMS) introduces a system-level recovery–postponement trade-off. Building on this representation, we propose a computationally efficient online mitigation strategy that addresses a twofold uncertainty, accounting for stochasticity in both exogenous disturbances and the system’s internal delay absorption capacities. We prove that the incremental method exactly maintains the current-information timing plan under monotone information updates. For a stylized time-invariant setting, we derive an event-class approximation of persistent delay growth and characterize the stabilization boundary within the fixed-graph sequence-preserving policy class. Numerical experiments on a CAV rhythmic-control network and an urban metro network demonstrate the framework’s versatility in enhancing robustness across both emerging and existing transportation paradigms.