Improved Snake Optimizer Approach to Enhance Traffic Signal Timing Optimization by SUMO

Introduction Drag on city traffic has escalated to be a pivotal problem in almost all cities in the world, and commuter travel time, fuel consumption and emission have also soared. Fixed-time signal control systems have been traditionally used in traffic signal control, but their optimality highly depends on the time of day they are applied and they are not capable of real-time traffic control. In order to overcome these drawbacks, intelligent and adaptive traffic control approaches are being explored by both scientists and traffic developers. Objective The purpose of this research is to create and assess a dynamic traffic light control system by utilizing Snake Optimization (SO), a novel meta-heuristic algorithm. In order to minimize vehicle queues, cut down on delays, and enhance overall traffic flow, the main goal is to examine how well SO optimizes traffic signal timings based on real-time traffic data. by contrasting conventional static control techniques and meta-heuristics with the SO-based dynamic approach. Proposed Method To find the best traffic light phase durations at intersections, the suggested approach uses the Snake Optimization (SO) algorithm, a bio-inspired meta-heuristic technique. Real traffic data from the Algerian city of Algiers, which depicts realistic traffic volumes and patterns, is used to test the method. The SUMO (Simulation of Urban Mobility) simulator, an open-source program popular for simulating microscopic traffic, is used to construct the experimental framework. Traffic scenario modeling, control algorithm implementation, and performance evaluation in a simulated urban setting are all made possible by SUMO. The following steps are part of the optimization process: gathering of data, using the SUMO simulator to model traffic flow and intersections, incorporating the SO algorithm to modify traffic light timings in real time in response to inputs, evaluation of performance using metrics like vehicle throughput, average waiting time, reduction of CO2 emission and total travel time, and evaluation of the benefits obtained by the SO-based approach through comparison with a baseline static control system and ACO-DSOS meta-heuristic. Conclusion According to the simulation results, the suggested approach, which is based on the Snake Optimization algorithm, performs better than other meta-heuristics and conventional static control systems. The algorithm's effectiveness in controlling traffic flows is validated by quantitative comparisons. According to the study's findings, SO is a practical and successful strategy for maximizing traffic light control in practical settings.