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城轨运营中断下应急公交车辆调度模型
引用本文:王佳冬,袁振洲,宁尚彬.城轨运营中断下应急公交车辆调度模型[J].交通运输系统工程与信息,2001,19(4):149-154.
作者姓名:王佳冬  袁振洲  宁尚彬
作者单位:北京交通大学 城市交通复杂系统理论与技术教育部重点实验室,北京 100044
基金项目:国家重点基础研究发展计划/National Key Basic Research Program of China(2012CB725403).
摘    要:随着城市轨道交通运营网络规模扩大和客流迅速增长,运营中断下公交应急联动问题日益被重视.针对城轨运营中断下的公交桥接疏运问题,提出一种灵活调度策略,允许车辆服务于不同的桥接路径,以最小化总疏散时间和平均乘客延误为目标,建立基于灵活路径模式下的多目标应急公交车辆调度模型,使用理想点法和遗传算法进行求解,并进行实例验证.对比传统固定路径车辆调度方案,基于灵活路径的车辆调度方案使总疏散时间和平均乘客延误分别减少了 4.2%和 4.4%.结果表明,本文提出的模型能够提高公交应急桥接疏运效率、降低乘客延误.

关 键 词:城市交通  应急公交响应  多目标优化  地铁运营中断  遗传算法  
收稿时间:2018-11-23

Optimization Model of Emergency Bus Dispatching in Response to Operational Disruptions of Urban Rail Transit
WANG Jia-dong,YUAN Zhen-zhou,NING Shang-bin.Optimization Model of Emergency Bus Dispatching in Response to Operational Disruptions of Urban Rail Transit[J].Transportation Systems Engineering and Information,2001,19(4):149-154.
Authors:WANG Jia-dong  YUAN Zhen-zhou  NING Shang-bin
Institution:MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
Abstract:Along with the urban rail transit network expansion and rapid growth in passenger flow, the risk of passengers stranded at the station and emergency response problem are getting more attention. A flexible dispatching strategy was proposed to solve the bus bridging problem in the operational disruption of urban rail transit. Buses are allowed to flexibly serve different bridging routes. An optimization model of multi- objective programming based on flexible routes was proposed to minimize the total evacuation time and average travel delay of all stranded passengers. The model was solved through the ideal point method and genetic algorithm. A case study was carried out and analyzed. Compared with the traditional strategy of fixed routes, the total evacuation time and average travel delay are respectively reduced by 4.2% and 4.4% by this optimized bus dispatching strategy. The results suggested that our approach can improve the efficiency of bus bridging and reduce travel delay.
Keywords:urban traffic  emergency bus response  multi-objective optimization  metro operational disruptions  genetic algorithm  
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