Vehicle-pedestrian optimization framework for exposure-aware routing

Vehicular traffic is a major source of air pollution in urban areas, exposing pedestrians and residents to harmful emissions. Recent works have proposed exposure-aware pedestrian routing strategies based on static emission maps. In this study, we extend this approach to a dynamic, multi-agent simulation framework involving both cars and pedestrians. Starting from the initial fastest-path routing, we simulate the co-evolution of vehicular emissions and pedestrian exposure over multiple steps, where pedestrian flows dynamically influence car emissions, and vice versa. Two routing strategies are explored: global weighting, where a shared trade-off between travel time and exposure is selected, and local weighting, where each trip independently chooses its optimal trade-off. Experiments conducted on real-world urban data from a medium-sized city in Italy demonstrate that both strategies achieve significant reductions in pedestrian exposure; however, they differ in their impact on vehicle emissions and travel times. Global weighting yields more coordinated adaptation but at a higher systemic cost, while local weighting achieves more balanced outcomes with lower disruption. These results provide insights into designing urban routing policies that jointly optimize mobility efficiency and environmental sustainability.