Abstract:

This study presents a pioneering hybrid optimization framework designed to enhance urban transportation efficiency in Sokoto City, leveraging a synergistic combination of population-based algorithms and mathematical refinement techniques. By integrating Metaheuristic approaches (Genetic Algorithm and Ant Colony Optimization) with Mathheuristic methods (Mixed Integer Linear Programming with GurobiPy), the model optimizes bus terminal placement, bus stop allocation, and vehicle routing, utilizing a graph-based road network and real-time traffic data. The results reveal significant improvements, including a 33% reduction in travel time, a 25% decrease in congestion delays, and an 18% fuel cost saving. These findings demonstrate the hybrid model's superiority over traditional routing methods and standalone heuristic techniques, positioning it as a valuable tool for transportation planning in similar urban environments. This research contributes to the advancement of intelligent urban mobility and provides actionable insights for policymakers, transportation authorities, and urban planners in emerging economies.