Analysis of Bus Scheduling System Based on Genetic Algorithm

With urban transportation infrastructure development lagging behind the rapid growth of traffic demands, urban traffic conditions continue to deteriorate. Varying degrees of traffic congestion occur at major intersections and high-volume roads, creating a bottleneck for urban development. Multiple solutions exist to address urban traffic issues, with vehicle restriction policies being one effective approach. By limiting vehicle usage, public transit ridership increases, alleviating urban congestion. However, this introduces new challenges in bus scheduling optimization. The bus scheduling system must account for complex factors including passenger flow, vehicle availability, and road network constraints, requiring advanced computational methods like genetic algorithms for optimal solutions. Intelligent Transportation Systems (ITS) have emerged as key technological frameworks for addressing these challenges.

For intelligent bus dispatch problems, generating bus departure timetables constitutes the core of urban bus scheduling. These timetables serve as fundamental references for daily vehicle dispatch operations and provide working guidelines for schedulers and drivers. Effective timetable generation must consider multiple factors: bus fleet size, passenger travel demand patterns, and real-time road congestion levels. Implementing an optimized timetable enables transit enterprises to improve vehicle utilization efficiency, reduce operational costs, and minimize passenger waiting times through computational optimization techniques. Within the ITS framework, genetic algorithm implementations can evolve timetable solutions by evaluating fitness functions that balance operational constraints and service quality metrics, contributing significantly to urban transportation development.