Multi-modal Routing in Urban Transportation Network Using Multi-objective Quantum Particle Swarm Optimization

This study focuses on addressing the routing challenges within urban transportation networks using a multi-objective Quantum Particle Swarm optimization (MOQPSO) approach. The transportation network under consideration encompasses subways, buses, taxis, and walking routes. The objective is to minimize the overall route length while ensuring efficiency through the incorporation of five specific changes along the route. The routing problem inherently represents an optimization challenge characterized by a discrete search space. To tackle this problem using the MOQPSO approach, discrete state handling techniques are implemented. Here, a quantum-behaved concept is assigned to enhance the performance of the PSO. To evaluate the proposed approach, simulated data is generated, consisting of 200 stations randomly distributed across a surface of 1600 km2, thus creating a multi-modal network. Comparative analysis is performed between the improved PSO algorithm and its standard version, and the results highlight the MOQPSO algorithm's ability to discover the optimal solution within a limited number of iterations and time. Furthermore, the routes determined by the MOQPSO are found to be more efficient. Conclusively, this study demonstrates that the MOQPSO approach outperforms Pareto solution of the standard MOPSO and NSGAII in the multi-modal routing problem within urban transportation networks.