Optimization Model for Electric Bus Scheduling Based on OD Data and Improved Genetic Algorithm

In order to achieve a more efficient bus scheduling management, a scheduling optimization model for electric buses based on Origin-Destination (OD) data and improved genetic algorithm is proposed. Compared to previous studies that mainly relied on boarding card data, this paper employs OD data, which better reflects changes in passenger flow, to guide the development of more reasonable bus scheduling. Unlike fuel-powered buses that primarily rely on gasoline as their energy source, electric buses rely primarily on electricity. Therefore, there are significant differences in considering operational costs and constraints when scheduling electric buses. This paper firstly combines the OD data of passenger flow on bus routes and the characteristics of electric buses powered by electricity to establish a scheduling optimization model that comprehensively considers the operational costs of the bus company and passenger satisfaction. Secondly, an improved adaptive genetic algorithm is proposed. This algorithm improves the crossover and mutation operators of the traditional adaptive genetic algorithm by introducing a descent probability function and a fixed probability, thereby enhancing the global optimization capability during the optimization process. Finally, the model and algorithm are verified and analyzed using the example of Route 401 in a certain city. The results show that the improved algorithm outperforms traditional algorithms in terms of optimization results and efficiency; the optimized scheduling plan has reduced the overall costs for passengers and the bus company by 8.3% compared to pre-optimized, enhancing the attractiveness and competitive advantage of the bus service.