A Bi-Objective Traffic Signal Optimization Model for Mixed Traffic Concerning Pedestrian Delays

Urban traffic networks suffer in numerous ways from traffic congestion. Some of these adverse effects are increased travel times of cars, buses, bicycle users, pedestrians etc., with the addition of excessive greenhouse gas emissions. Transportation engineers and policy makers try to improve the quality of urban transportation systems by developing projects to enhance the pedestrian experience, reduce private car usage, reduce total time spent in the network through different control strategies, and diminish the detrimental effects. In this context, this study takes Connected and Automated Vehicles (CAVs) and pedestrians into account at signal-controlled intersections. A novel intersection signal control optimization methodology that incorporates pedestrian delays and vehicular emissions from CAVs is presented. Non-dominated sorting genetic algorithm-II is utilized to solve the multiobjective optimization problem. For the emission calculations, the MOVES3 emission model is utilized. The proposed framework is tested on real-world case study. Simulation experiments showed major improvements in pedestrian delays and lower emissions.