Enhanced resilience in multimodal passenger transport through digital technologies and generative and discriminative AI

Scope: Passenger transport systems are a critical component of urban, sub-urban, and long-distance travel, facilitating connectivity, reducing traffic congestion, and supporting environmental sustainability. However, these systems face challenges such as operational disruptions, safety and security risks, and evolving passenger demands. In the context of passenger transport, resilience refers to the ability of a transport system to absorb disturbances, maintain its basic structure and function, and recover to a required level of service within acceptable time and cost after being affected by disruption. This involves implementing measures that ensure continuous operation, quick recovery from unexpected events, and adaptability to changing circumstances. Enhanced resilience is essential for maintaining public trust and ensuring smooth functioning of transport systems and it is a cornerstone of the EU Sustainable and Smart Mobility Strategy. Proposed actions are expected to address all of the following aspects: Development and implementation of measures to enhance the resilience of urban, sub-urban and long-distance transport systems. Leveraging digital technologies (e.g. big data, digital twins) and in particular generative and discriminative AI for anticipating and predicting the evolution of disruptions and their impacts, with real-time planning and information systems for minimizing impact and enabling faster recovery. Use of innovative technologies for data acquisition an…

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