站点可变型需求响应式公交车辆调度优化研究

Vehicle scheduling optimization for demand responsive transit with flexible stops

站点可变型需求响应式公交是一种既有固定服务站点又允许乘客在可变站点预约的公交运营模式.传统研究聚焦于单个车次的行程规划,而忽视车辆在整个运营周期执行多个车次的接续性.本研究从系统视角建立单个运营周期内车辆发车时刻表编制和车辆调度协同优化模型,以总成本最小化为目标,构建时空网络图,清晰表达站点可变型需求响应式公交的服务逻辑和复杂的时空约束.同时根据问题特征,整合8类邻域算子和改进的最大网络流算法,设计可快速求解现实大规模复杂问题的自适应变邻域搜索算法.以中国广州市黄埔区部分路网为例进行数值实验,结果表明,相比传统两阶段车辆调度模型,本模型车辆使用率提高8.7%以上,总成本下降20%~70%;相比传统算法,在绝大多数订单规模下自适应变邻域搜索算法求解质量更优,在预约订单规模为60时,可节省总成本30%以上.

Flexible stops demand responsive transit is a public transportation mode that combines fixed service stops with the ability for passengers to book at flexible stops.Traditional research focuses on the planning of individual trips,while ignoring the connectivity of vehicles performing multiple trips throughout the entire operating period.This paper establishes a joint model for timetabling and vehicle scheduling within a single period,with the objective of minimizing total cost.Our model utilizes a time-space network to represent the service logic and constraints of flexible stops demand responsive transit service.Furthermore,based on the problem characteristics,an adaptive variable neighborhood search algorithm(AVNS)that integrates 8 types of neighborhood operators and an improved maximum network algorithm is designed to efficiently solve real-world large-scale complex problems.Finally,numerical experiments conducted in Guangzhou's Huangpu District demonstrate that bus utilization rate can enhance 8.7%and total cost can reduce 20%to 70%,respectively,compared with traditional two-stage method.Besides,the AVNS provides better solution quality for the vast majority of order sizes,saving over 30%of total cost for an order size of 60,compared with traditional algorithms.