ECODRIVE: eco-driving to reduce infrastructures vehicular emissions

This thesis investigates the potential of intelligent traffic management systems to mitigate the environmental impact of private road transport without compromising the operational efficiency of road infrastructure. The primary objective is to assess how specific traffic control policies can reduce both noise and air pollution, while ensuring acceptable traffic flow conditions. A simulation-based methodological framework was developed, integrating microscopic traffic models with emission models for both noise and air pollutants. This framework was first applied to a hypothetical Toy Network and later validated on a real case study: the Grande Raccordo Anulare (GRA) in Rome. The tested policies included differentiated speed limits, dynamic lane usage (including selective opening of emergency lanes), and variations in traffic demand and vehicle fleet composition. Their effectiveness was evaluated in terms of reductions in emissions, energy consumption, and impact on levels of service. Results show that noise emissions can be reduced by up to 15% on the Toy Network, with consistent trends confirmed on the Real Network, demonstrating the scalability of the proposed strategies. However, energy consumption reductions remained limited to about 7%, highlighting the challenges of improving energy efficiency in dense urban environments. To identify optimal policy combinations, two optimization algorithms—Local Search and Coordinate Descent—were implemented. These algorithms successfully balanced environmental benefits and traffic performance, identifying targeted speed limit strategies and controlled emergency lane openings as the most effective measures. This research contributes to the field of sustainable mobility by addressing the dual challenge of environmental protection and infrastructure efficiency. It adopts an integrated mitigation approach, capable of simultaneously addressing noise and air pollution. The proposed methodology is flexible, adaptable to different network types, and offers practical support for decision-makers in designing environmentally conscious traffic management strategies. The findings reinforce the idea that intelligent traffic systems can play a pivotal role in reducing the environmental footprint of road transport, without sacrificing usability and service quality of road infrastructure.