MSCSO: A Hybrid Nature-Inspired Algorithm for High-Dimensional Traffic Optimization in Urban Environments

Metropolitan regions have experienced higher economical and environmental pressure due to the fasted urbanization leading to increased traffic jams that necessitate the use of higher optimization techniques. Traditional traffic models do not usually take large-dimensional and dynamicity of urban mobility into consideration and require extraordinary computational approaches. Modified Sand Cat Swarm Optimization (MSCSO) improves the Sand Cat Swarm Optimization (SCSO) algorithm that adds Levy flights to global exploration and roulette wheel selection to adaptive exploitation to solve problems that are complex and high-dimensional. When used in urban traffic management, MSCSO works with enormous volumes of traffic, speed, weather, and incident, all of which may decrease Travel Time Index by 15 percent during rush hours. Benchmark tests are used to prove that MSCSO is better, scoring 0.0 in Sphere, Ackley and Rastrigin functions, and 28.0753 in Rosenbrock, whereas higher scores belong to Particle Swarm Optimization, Genetic Algorithms, Ant Colony Optimization and SCSO (e.g., 46). It supports urban planning, since a Flask-based web interface has the possibility to input and visualize real time traffic data in a simple way. The success of MSCSO is reliant on high-quality data and hardware-friendly algorithms but can scale to use real-time data sources, such as from GPS, machine learning traffic projections, and cloud hosting, and is of potential use in logistics, energy delivery, and resource assignment.