The Impact of Adaptive Signal Control on Traffic Efficiency and Environmental Pollution

Abstract

This master’s thesis investigates the impact of adaptive signal control algorithm, partic ularly the Max-Pressure (MP) algorithm, on traffic efficiency and environmental pollu tion within urban areas. Utilizing real-world data from Gothenburg, Sweden, a simu lation model was developed in Simulation of Urban MObility (SUMO) to evaluate the performance of adaptive signal control against fixed-time control. The study focuses on key metrics such as average vehicle delay, capacity, fuel consumption, and emissions of pollutants including CO2, CO, NOx, HC, and PMx. Results demonstrate that adaptive signal control significantly improves traffic flow, especially under high-demand condi tions or in the presence of incidents. The average vehicle delay was reduced by 27% by the adaptive signal control at network level, and 38% at a single intersection. The network capacity was increased by 15% by the adaptive signal control. Emission levels and fuel consumption also show notable reductions, with improvement rates reaching up to 46% at a single intersection, or up to 23% for the whole network. The findings exhibit the potential of adaptive signal control to support sustainable urban mobility and provide a foundation for developing solutions that address environmental targets amid growing traffic demands.