Optimization of Student Public Transportation Routes Using the Particle Swarm Optimization

Abstract

Student public transportation often experiences inefficiency due to suboptimal routes and schedules. This study aims to determine more efficient student transportation routes and schedules using the Particle Swarm Optimization (PSO) method. This method is applied to minimize travel time and distance while considering fleet capacity and operational constraints. Simulation results show that PSO can significantly improve objective values, resulting in more organized and efficient routes and departure schedules. This study concludes that PSO is an effective optimization approach for student transportation planning. The contribution of this research is to provide a computational optimization model that can be used as a basis for developing a more adaptive transportation scheduling system. In addition, the optimization model developed is capable of integrating operational variables such as departure sequence, passenger readiness, and passenger distribution patterns on each bus to produce more realistic solutions. The application of PSO in the simulation shows that this algorithm can eliminate hard constraints while reducing soft constraints, reflecting an increase in overall system efficiency. The departure schedule obtained is also more stable with consistent headways, thereby reducing student waiting times and increasing fleet utilization.