The CONDUCTOR project’s main goal is to design, integrate and demonstrate advanced, high‐level traffic and fleet management that will allow efficient and globally optimal transport of passengers and goods, while ensuring seamless multi‐modality and interoperability. Using 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. We 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. These innovations will lead to less urban traffic and congestion, lowered pollution, and a higher quality of life. Project innovations will be integrated into a common, open platform, and validated in three use cases, testing the interoperability of traffic management systems and integration of different transportation means for both people and goods.
How We Contribute
The DSAIT team designs and simulates innovative traffic control solutions and specifically dynamic space allocation and network-level traffic signal optimization, for future cooperative and connected mobility. By leveraging real traffic data from the city of Athens within a Reinforcement Learning framework, DSAIT implements these strategies in a large-scale microsimulation environment to demonstrate their potential to improve traffic efficiency, reduce congestion and support multimodal mobility.
