Mobility Infrastructure Optimization  via Stochastic  Relaxation the Traffic Flow Reconstruction

Abstract Urban traffic congestion and pollution represent critical challenges for sustainable city development. In this paper, we presents a novel approach to optimizing urban mobility infrastructure using a stochastic relaxation algorithm. The method is designed to address the growing challenges of traffic congestion, fuel consumption, and CO2 emissions in rapidly urbanizing cities. The system automates the generation and evaluation of road network modifications, drastically reducing the time and effort required for traditional simulation-based approaches. By utilizing real-time traffic data and traffic flow reconstruction algorithms within the Snap4City platform, the proposed system allows for the efficient exploration of "what-if" scenarios, optimizing key performance indicators such as travel time, fuel use, and emissions. A case study conducted in Florence, Italy, demonstrates significant improvements in traffic conditions, fuel efficiency, and emissions reduction, underscoring the system's potential to enhance urban mobility in a sustainable manner. This system has been developed exploiting the Snap4City platform Scenario Editor and platform.