基于不确定理论的常规公交车辆调度优化

为提高公交车的利用效率，本文将公交车辆调度方案划分为高峰型和平峰型。在考虑乘 客候车时间与站间运行时间不确定的现实条件下，综合考虑不同车型的运营成本和乘客候车成 本；基于不确定理论建立混合车型下的双重不确定多目标规划模型，并通过遗传算法的python编 码求解。以南昌市211路公交上行为例，进行sumo仿真结果表明：在保证公交持续运营的前提 下，调整车辆调度方案有助于降低成本和提高运行效率；在高峰期，将发车间隔降低25%，公交车 统一使用纯电动客车，总成本降低5%，平均延误减少4%；平峰期，总成本降低10%，平均延误降 低3%。两组仿真结果发现，考虑不确定因素的公交车辆合理调度安排有利于充分利用公交车辆 资源和提高运行效率。

Optimization of Regular Bus Scheduling Based on Uncertainty Theory

To improve the utilization of buses, two bus dispatching schemes are designed for peak and non-peak hours. Under the uncertainty of passenger waiting time, inter- station running time, and operation cost of different types of vehicles, an uncertain multi- objective programming model for hybrid vehicles is established based on the theory of uncertainty. The model was solved by a genetic algorithm, performed in Python. Taking the No.211 bus in Nanchang City as an example, simulation results showed that, adjusting the adjustment of the vehicle schedule can reduce the operation cost and improve the operation efficiency based on the continuous operation of the buses. In the peak period, with the use of pure electric buses and the departure interval reduced by 25%, the total cost will be reduced by 5% and the average delay will be reduced by 4%. During the non-peak period, the total cost is reduced by 10% and the average delay is reduced by 3%. With the two sets of simulations, it was found that the reasonable schedule of public transport vehicles considering uncertainty is conducive to making full use of resources and improving operation efficiency