{"id":366,"date":"2022-11-01T14:57:00","date_gmt":"2022-11-01T12:57:00","guid":{"rendered":"https:\/\/previews.cantaloop.gr\/dsait\/?post_type=project&p=366"},"modified":"2026-05-12T23:42:48","modified_gmt":"2026-05-12T20:42:48","slug":"conductor","status":"publish","type":"project","link":"https:\/\/www.transport.ntua.gr\/dsait\/project\/conductor","title":{"rendered":"Fleet And Traffic Management Systems For Conducting Future Cooperative Mobility"},"content":{"rendered":"\n

The CONDUCTOR project\u2019s main goal is to design, integrate and demonstrate advanced, high\u2010level traffic and fleet management<\/a> that will allow efficient and globally optimal transport of passengers and goods, while ensuring seamless multi\u2010modality and interoperability. Using innovative dynamic balancing and priority\u2010based management of vehicles (automated<\/a> and conventional) CONDUCTOR will build upon state-of-the-art fleet and traffic management solutions in the CCAM<\/a> 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 \u201cconductors\u201d 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.<\/p>\n\n\n\n

How We Contribute<\/mark><\/h2>\n\n\n\n

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<\/a> framework, DSAIT implements these strategies in a large-scale microsimulation<\/a> environment to demonstrate their potential to improve traffic efficiency, reduce congestion and support multimodal mobility<\/a>.<\/p>\n\n\n\n

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