摘要
为解决机场枢纽旅客疏散需求与陆侧交通运力供给的精准适配难题,该文在分析运力配置与旅客出行方式选择相互作用机理的基础上,建立了面向大型机场陆侧大巴、轨道交通、出租车和私家车等多交通方式协同的运力匹配双层规划模型。首先,以出行时间、出行费用、准时度和舒适度4个指标作为特征变量构建了多交通方式选择效用函数。其次,综合考虑各交通方式的协同与服务水平特点,建立了运力匹配双层规划模型:上层模型以企业运营成本、旅客候车成本和碳排放环境成本三者之和最小化为目标,对公共交通线路的发车时间间隔、出租车到达率进行优化;下层模型基于随机用户均衡-Logit模型,在上层生成的运力配置方案的基础上,实现客流面向多交通方式的分配。再次,该文提出了一种改进的遗传算法以求解模型,通过嵌套连续平均算法与预搜索机制提高下层模型的计算效率,进而提升综合求解效率。最后,以北京大兴国际机场为例开展实证研究,结果表明:所构建的双层规划模型和算法能有效优化大型机场交通运力资源配置,从而达到优化陆侧交通结构、倡导绿色出行的目的。
[Objective]In the airport ground-transport system,it is operationally important to match the evacuation requirement of passengers,and the capacity of multimodal transport vehicles is crucial.Numerous studies have investigated single-mode transport capacity allocation;however,research on multimode allocation is scarce.[Methods]To mitigate the difficulty in realizing an exact match between the evacuation demand of passengers and the capacity of multimodal transport,a bi-level programming model for multimodal transport resource allocation is proposed according to the analysis of the interaction between capacity allocation and passenger travel choice.A utility function of multiple travel modes,including airport buses,metro,taxis,and private cars,is formulated with the following four features:travel time,travel cost,punctuality,and comfort.The upper-level objective is to minimize the total enterprise-operation cost,passenger-waiting cost,and carbon emission cost for optimizing the headway of public transit and the taxi arrival rate,which is subject to the capacity of each transit mode,the range of each public-transit headway decided by fixed equipment,and the range of taxi arrival rate decided by the capacity of boarding location.Based on the output of the transport capacity allocation scheme used by the upper level,the low-level objective is to assign the passenger flow toward multiple travel modes according to a stochastic user equilibrium-logit model with a utility function.Furthermore,an improved genetic algorithm combined with method of successive algorithm(MSA)is designed to solve the proposed bi-level programming model.To improve the solving efficiency of the algorithm,a pre-search mechanism is proposed,in which the infeasible solution is filtered out using low-precision MSA to reduce the computational cost of repeatedly calling the low-level model.[Results]The Beijing Daxing International Airport was considered as a case study to illustrate the efficiency and effectiveness of the proposed bi-level programming
作者
黄爱玲
王子吉安
张哲
李名杰
宋悦
HUANG Ailing;WANG Zijian;ZHANG Zhe;LI Mingjie;SONG Yue(Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China;Department of Information Management,Beijing Daxing International Airport of Capital Airport Group Co.,Ltd.,Beijing 102600,China)
出处
《清华大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2023年第11期1729-1740,共12页
Journal of Tsinghua University(Science and Technology)
基金
国家重点研发计划项目(2018YFB1601200)
国家自然科学基金创新群体项目(71621001)。
关键词
机场枢纽
运力匹配
双层规划
改进遗传算法
随机用户均衡
连续平均算法
airport hub
transportation capacity matching
bilevel programming
improved genetic algorithm
stochastic user equilibrium
method of successive algorithm
