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 on real-world urban data of a medium-sized city in Italy show that both strategies achieve significant reductions in pedestrian exposure, but 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.