Resilient multimodal transport management with shared autonomous electric vehicles under disruptions – RAISE

Natural disasters, technological failures, and human-caused accidents frequently disrupt public transport lines and infrastructure. This may result in shutdowns of public transport services and shift of travel demand to road networks. Without effective emergency response strategies, urban traffic systems can easily become paralyzed. Emerging technologies of shared autonomous electric vehicles (SAEVs) and intelligent infrastructure offer promising solutions in these nonrecurrent situations. This collaborative project aims to develop a resilient, multimodal transport management strategy that leverages automation and communication to optimize on-demand public transport services and traffic flow during disruptions, with a central focus on enhancing the resilience of urban transport networks to adapt and recover swiftly from unexpected events. The primary research question is how to jointly design real-time fleet management strategies for SAEVs to serve stranded passengers, alongside adaptive signal and lane access control strategies to minimize negative impacts on background road traffic. To address this, we propose a hierarchical three-level decision and control framework for multimodal transport management. The first level (demand-supply balancing) dynamically optimizes SAEV fleet size to match demand, the second level (vehicle management) determines routing and charging locations and times for SAEVs, and the third level (infrastructure management) jointly optimizes spatial and temporal SAEV dedicated lane allocations and network-wide green time allocation. This framework will build on a new macroscopic multimodal, multiclass, and multilane traffic flow model developed in this project, which simulates traffic dynamics under multimodal interactions and provides quantitative evaluation metrics in terms of transport system resilience, mobility service quality, and traffic flow performance.
The proposed fleet and traffic management strategies will be validated in large-scale simulation-based case studies in Lyon under a metro line disruption and in Dresden under a bridge closure affecting trams and vehicles, to demonstrate their practical applicability and resilience benefits in real-world scenarios. The project will deliver real-time transport management strategies, a fast simulation tool that predicts multimodal transport system responses for a wide range of scenarios, and a set of evidence-based recommendations for cities and transport operators to deal with disruptions. These outputs will provide a framework to leverage the potential of shared autonomous electric mobility systems and strengthen the competence of cities and public transport operators in dealing with disruptions.