Modeling and quantifying interaction of information and capacity in public transport disruptions

Disruption in public transport networks has adverse implications for both passengers and service managers. To evaluate the effects of disruptions on passengers’ behaviour, various methods, simulation modules, and mathematical models are widely used. However, such methods included many assumptions for the sake of simplicity. We here use multiagent microsimulation modules to simulate complex real-life scenarios. Aspects that were never explicitly modelled together are the capacity of the network and the effect of disruption to on-board passengers, who might need to alight the disrupted services. In addition, our simulation and developed module provide a framework that can be applied for both transport planning and real-time management of disruption for the large-scale network. We formalize the agent-based assignment problem in capacitated transit networks for disrupted situations, where some information is available about the disruption. We extend a microsimulation environment to quantify precisely the impact and the number of agents directly and indirectly affected by the disruption, respectively, those passengers who cannot perform their trip because of disrupted services (directly affected passengers), and those passengers whose services are not disrupted but experience additional crowding effects (indirectly affected passengers). The outcomes are discussed both from passengers’ perspective and for extracting more general planning and policy recommendations. The modeling and solution approaches are applied to the multimodal public transport system of Zürich, Switzerland. Our results show that different information dissemination strategies have a large impact on direct and indirect effects. By earlier information dissemination, the direct effects get milder but larger in space, and indirect negative effects arise. The scenarios with the least information instead are very strongly affecting few passengers, while the less negative indirect effect for the rest of the network.ISSN:1979-2016ISSN:2042-319