Resilience enhancement of urban public transport networks: modeling and optimizing recovery from acute disruptions

Urban public transport systems are essential for sustaining economic activity, social interaction, and urban development. Rising exposure to extreme weather, infrastructure failures, and public emergencies has exposed the vulnerability of multi-modal transit networks: because rail, bus, and feeder systems are tightly coupled, a localized disruption can cascade across the network within minutes and degrade mobility for hours. Conventional planning has optimized for efficiency and capacity, while resilience-oriented strategies for the operational response phase — the window from the moment a disruption hits to full service restoration — remain underdeveloped.This Research Topic aims to advance both the theoretical and the practical understanding of resilience enhancement in urban public transport systems during the operational response phase. It provides an interdisciplinary platform for researchers and practitioners to present new methodologies for assessing, modeling, and improving transport resilience under disruptive conditions, with particular emphasis on the absorptive, adaptive, and restorative capacities of multi-modal transit networks during and immediately after acute disruption events.The topic encourages studies that integrate transportation engineering, operations research, and disaster management, and that bridge methodological development with empirical evidence from real disruption events so that operators, dispatchers, and emergency-response agencies can apply the results in practice.We welcome empirical, theoretical, and methodological contributions that explore the multifaceted nature of transport resilience, with particular emphasis on multi-modal integration, recovery optimization, and the role of data and emerging technologies. 1. Modeling frameworks for disruption and recovery• Cross-modal congestion propagation between rail, bus, and feeder systems• Complex-network and cascading-failure models of multi-modal transit• Stochastic programming, robust optimization, and scenario-based formulations under demand and supply uncertainty• Resilience metrics, recovery-curve benchmarking, and standardized evaluation across disruption classes2. Operational recovery strategies• Bus-bridging and extended bridging service design under uncertainty• Turn-back, short-turn, and line-truncation strategies• Vehicle, crew, gate, and platform reassignment during disruption• Real-time passenger guidance and demand redistribution across modes3. Pre-disruption preparedness• Scenario-based allocation of protective resources (spare fleet, standby crews, reserve capacity)• Vulnerability assessment and risk-based prioritization of critical links and stations• Joint preparedness planning between transit operators and emergency-response agencies4. Data-driven approaches and emerging technologies• Digital twins of urban transit networks for disruption simulation and what-if analysis• AI and machine-learning models for disruption prediction, propagation forecasting, and dispatch decision support• Big-data analytics on smart-card, AVL, GTFS-realtime, and trajectory data for resilience assessment• IoT-based real-time sensing of crowding, vehicle state, and infrastructure health• Blockchain and secure data-sharing architectures for multi-operator coordination• Empirical case studies of real disruption events using the data sources above Topic editors: Jinqu Chen, Cheng Zhang, Shaochuan Zhu Source: https://www.frontiersin.org/research-topics/81505/resilience-enhancement-of-urban-public-transport-networks-modeling-and-optimizing-recovery-from-acute-disruptions