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 multimodality and
interoperability, through dynamic balancing and priority-based management of vehicles (automated and conventional).
We will upgrade existing models and technologies to fit future mobility needs placing autonomous vehicles at the centre of future cities. The demonstrations, through mixed traffic orchestration, will mostly rely on public transportation, as the amount of private autonomous cars is still expected to be relatively low. The main driving force in demonstrations will be the consideration of users’ social needs.
Within the upgraded fleet simulation environment we aim to investigate fleet management scenarios for a multipurpose transport system combining passengers and goods transportation. The fleet control model (including public transit and logistics) will be coupled with a multi-resolution traffic simulation model to evaluate network-wide effects of the implemented fleet management strategies for demand-responsive mobility services for people and goods.
The proposed idea of ride-parcel-pooling aims at utilizing unused capacities by combining passengers and goods transportation flows within one transport system. We will also implement and evaluate cooperative routing strategies (social routing) for large scale CCAM vehicle fleets and aim at balancing the traffic loads on the overall road network.
The multi-modal system (combining different transportation modes such as pedestrians, connected/autonomous cars, motorcycles, taxis, delivery cargo vehicles, buses, etc.) will be optimised based on the user needs to ensure transport resilience and business continuity. Today’s city transport strategies are focusing on the efficient movement of people and goods and are prioritizing sustainable modes.