How is it achieved?
CONDUCTOR will use innovative dynamic balancing and priority‐based management of vehicles, automated and conventional. CONDUCTOR will build upon state-of-the-art fleet and traffic management solutions in the CCAM ecosystem and develop next generation simulation models and tools enabled by machine learning and data fusion, enhancing the capabilities of transport authorities and operators, allowing them to become “conductors” of future mobility networks. The project will upgrade existing technologies to place autonomous vehicles at the centre of future cities, allowing heightened safety and flexible, responsive, centralized control able to conduct traffic and fleets at a high level.
What was our contribution?
Deusto participates in through Deusto Smart Mobility research team from the Faculty of Engineering. The research team has wide expertise in artificial intelligence techniques, particularly in optimization techniques (e.g. metaheuristics) and machine learning (e.g. deep learning, fuzzy systems) applied to transportation. Implementing functionalities such as, traffic patterns analysis for identifying congestion prediction and bottlenecks; extraction and comparison of mobility patterns based on the fusion of heterogeneous data sources (transport supply& demand, maps& cartography, sociodemographic and travel time information), collection of data sources and harmonization; route planning for passengers transport. In CONDUCTOR, Deusto will research on optimizing the transport network management through model-based optimisation and deep reinforcement learning techniques, upgrading existing solutions.
This project has received funding from the European Union’s Horizon Europe Research and Innovation Programme under Grant Agreement Nº 101077049