MULTI-OBJECTIVE OPTIMIZATION OF TRAFFIC SIGNAL TIMINGS FOR MINIMIZING WAITING TIME, CO2 EMISSIONS, AND FUEL CONSUMPTION AT INTERSECTIONS

Abstract

In this paper, we present an optimization strategy that uses Genetic Algorithms (GA) to reduce Waiting Time, CO2 Emissions, and fuel consumption in a transportation network. The optimization process is performed through simulations using the well-known traffic simulation tool, SUMO (Simulation of Urban MObility). The main objective is to identify the most efficient traffic light timing plan. The proposed GA uses a binary encoding method for signal timing configurations and includes a fitness function to evaluate network performance regarding fuel consumption. The algorithm iteratively builds a set of signal-timing solutions over generations until it converges on the optimal solution. Empirical results show that the GA approach significantly reduces waiting time, reducing CO2 emissions, and reducing fuel consumption compared to standard signal timing plans. This research makes a significant contribution to the domain of traffic management and provides valuable insights for policymakers and transportation planners looking to reduce the environmental footprint of urban transportation networks.

Keywords

• genetic algorithm
• optimization
• signal timing optimization
• sumo