Intelligent Multi-modal Transfer Flow Management system

ended

In sustainable transport systems, the role of multi-modal trips is increasingly important. Access, egress, and transferring within or between modes is an important source of trip disutility. The transfer experience needs to be substantially improved for multi-modal trips to be able to compete with car travel. This proposal focusses on making transfers at multi-modal transfer hubs as efficient, reliable and safe for the traveller as possible within existing transfer hubs using advanced sensing and actuation technology, state-of-the-art control strategies and our knowledge of passenger behaviour. We valorise technical and scientific results of the ALLEGRO project and apply it to design and test a pedestrian traffic management system for multi-modal transfer hubs. At the basis of the system is a state-of-the-art real-time monitoring system, that will collect and combine data from different types of sensors to estimate, predict and diagnose the flows inside the hub. The state-of-the-art sensors provide data with different semantic properties, ensuring that a comprehensive estimate of the current situation can be provided. One of the unique elements is the integration of sentiment information, stemming from social data analytics developed in ALLEGRO. Using a multi-faceted diagnoses system allows identification of problems in terms of traveller comfort, efficiency, and safety. The models developed in ALLEGRO are used to provide short-term predictions based on real-time sensor data, which prevents myopic decision making based on the current situation in the transfer hub only. Off-line pilots have shown the feasibility of using predictive approaches to provide decision-makers with advice on which actions to take. We will further develop and validate these approaches by testing them in an integrated environment. The system will be at (at least) one hub that already has been outfitted with a large sensor base as part of the SMART Station program.