PRIN 2022 CALL. DECREE NO. 104 OF 2 FEBRUARY 2022
PROJECT TITLE: I CAN BE - Impact assessment of Cooperative and Automated vehicles on transportation Networks considering users’ Behaviours
CODICE CUP: F53D23001990006
Budget: € 66.600
PoliBA Research Unit Lead: Mario Marinelli
Other Research Units:
University of Naples (PI: Luigi Pariota)
University of Salerno (Resp. UR: Roberta Di Pace)
Brief project description
The I CAN BE project analyzed and modeled user behavior in the presence of cooperative and automated driving (CAV) technologies, developing traffic control strategies that explicitly take into account the reactions of drivers and passengers. The approach combined disaggregate and aggregate simulation methodologies to assess the impact of CAV technologies on realistic transport networks, considering different technology penetration scenarios and varying levels of cooperation between vehicles and infrastructure.
Purpose
The main objective was to provide a methodological and operational framework for assessing the effects of CAV technologies, taking into account actual user behavior, in order to support the development of traffic management strategies, minimize negative impacts on the network, and contribute to the definition of guidelines for the sustainable deployment of cooperative and automated vehicles.
Expected results
Definition of an integrated framework for traffic simulation and optimization in the presence of CAV.
Realistic modeling of driving behavior and travel choices in mixed scenarios.
Assessment of the effectiveness of cooperative strategies (e.g. GLOSA, VSL, assisted merging) in reducing congestion, emissions, and risks.
Support for the definition of tools for public decision-makers in the process of adopting CAV technologies.
Achieved results
The POLIBA unit contributed to the development of an advanced simulation environment for real-time traffic management, called the virtual Traffic Management Center (vTMC), capable of representing in an integrated manner the interaction among cooperative automated vehicles, intelligent infrastructure, and human-driven vehicles. This framework made it possible to simulate realistic mixed-traffic scenarios, highlighting the impact of different cooperative control strategies. In particular, POLIBA analyzed the effectiveness of interventions such as lane drop, cooperative speed regulation (VSL), merging assistance, and traffic signal phase management. Through simulations conducted with microscopic traffic models, it was demonstrated that increasing CAV penetration reduces congestion, emissions, and conflict risks in a non-linear manner, and that infrastructure-based strategies must be adaptive during technological transition phases. In detail, a simulation and optimization model for merging on motorway ramps (e.g., the Naples ring road) under infrastructure constraints was developed, highlighting the synergy between infrastructure control strategies and cooperative behaviors enabled by CAVs. The unit also integrated an optimization approach based on robust indicators (e.g., cumulative average delay) to balance performance between mainline flows and entering vehicles, improving fairness and efficiency. Finally, it made a substantial contribution to the integration of models and results into a common multi-scale framework shared with the other project units, laying the groundwork for the development of guidelines applicable at the national level.
