Modelling Passenger Behavior in Disrupted Multimodal Transport Networks

Transport networks face escalating compound disruptions — accidents, extreme weather, and infrastructure failures — that expose the limits of infrastructure-centric resilience paradigms. A critical gap persists: passenger behavioral adaptation, including route switching, mode transfer, and trip cancellation, reshapes how networks degrade and recover, yet remains inadequately incorporated in prevailing models. Multimodal systems further complicate this landscape, where cross-modal interdependencies generate both redundancy and vulnerability. This Research Topic advances the integration of behavioral dynamics, cascading-failure modelling, and adaptive policy design to foster transport systems that are resilient because they anticipate how people actually respond when the network is under stress.Current transport resilience research predominantly treats passenger behavior as a passive, static flow variable, overlooking the dynamic feedback between human adaptation decisions and network performance evolution during disruptions. This simplification leads to systematic misestimation of critical-infrastructure importance, cascade thresholds, and recovery trajectories, particularly in multimodal systems where cross-modal transfers can either buffer or amplify failures. Policy responses, moreover, remain anchored in static contingency planning rather than adaptive management that leverages real-time behavioral insight. This Research Topic establishes the passenger decision-to-network feedback loop as its central object of study — integrating behavioral dynamics into cascading-failure modelling, supporting the design of behavior-centric adaptive policies, and distinguishing the collection from infrastructure- and operations-centric resilience work.This Research Topic treats passenger behavior as an active driver of network dynamics, not a static flow variable. The focus is the two-way feedback between human adaptation decisions and the cascading degradation and recovery of multimodal networks during disruptions. Both modelling contributions and empirical, data-driven studies are welcome — including work based on surveys, revealed- and stated-preference data, field observations, and large-scale mobility datasets that quantify how people respond under stress. Studies that couple behavioral evidence or models to network-failure or recovery dynamics are particularly encouraged. Original Research, Methods, Review, and Policy and Practice Reviews papers are invited across the following themes: 1. Behavioral feedback in cascading failures — how route switching, mode transfer, and trip cancellation accelerate or buffer network degradation, and how cascade thresholds shift once human adaptation is modelled endogenously. 2. Modelling risk perception and real-time decision-making under sudden disruptions — quantifying passenger behavioral response, risk perception, and demand redistribution as measurable model inputs, and representing them in network performance models; empirical characterization from observed or stated behavior is welcome. 3. Cross-modal behavioral transitions under extreme weather — how passengers reallocate across rail, bus, active, and private modes, and how these shifts propagate vulnerability or redundancy through coupled networks, via model-based or data-driven analyses. 4. Behavior-coupled modelling and simulation of disruption scenarios — data-driven and agent-based approaches that embed adaptive behavior in degradation and recovery models. 5. Behavior-centric (adaptive) policy and resilience design — interventions that leverage real-time behavioral insight, contrasted with static contingency planning. Topic editors: Jing Li, Cen Zhang, Yiming Yang Source: https://www.frontiersin.org/research-topics/82634/modelling-passenger-behavior-in-disrupted-multimodal-transport-networks