AbstractElectric buses are environmentally friendly with the features of zero emissions and low noise levels. Therefore, it is an inevitable trend to replace traditional fuel buses with electric buses. In the urban transportation system, the most common electric bus scheduling mode is single-vehicle-type scheduling, where crowdedness in peak hours could impair the bus service level. In contrast, a high vacancy rate in off-peak hours might lead to a waste of resources. To balance efficiency and the bus service level, this paper built an electric bus scheduling model with multiple vehicle types distinguished by vehicle configurations, such as bus capacity, battery capacity, purchase cost, charging power, and electricity consumption rate. The optimization objective was to minimize the bus system costs, including bus depreciation costs, charging costs, and congestion time costs. Then, a genetic algorithm was designed to solve the model, and the model is verified by a case study of one bus line in Nanjing, China. Compared with the single-vehicle-type scheduling schemes, including large and small vehicle types, the experimental results showed that the proposed model can reduce total bus system costs by 3.52% and 6.85%, respectively. The research results can provide references for bus companies to formulate driving plans and configure vehicles.

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