摘要
为提高城市道路建设时序决策的鲁棒性,提出了城市道路建设时序决策优化的双层规划模型。模型假定出行需求在一定范围内扰动,上层规划是在有限资金的约束下寻求各建设阶段的系统总出行时间与系统总出行时间对出行需求的灵敏度之间的综合最小值,下层规划为各建设阶段的随机用户均衡配流。文中推导出了系统总出行时间对出行需求灵敏度的计算式,并给出了模型的求解算法。最后以一个测试路网为例,对基于系统总出行时间、基于灵敏度、基于系统总出行时间与灵敏度综合出行时间的决策优化模型进行了计算分析,结果显示3种决策优化模型均可寻求到各自目标最优的城市道路建设时序,但在需求不确定的情景下基于灵敏度、基于系统总出行时间与灵敏度综合出行时间的决策优化结果更具鲁棒性。
In order to improve the robustness of sequence decision in urban road construction,a bi-level programming model was proposed to optimize urban road construction sequence decision.The model assumes that travel demand disturbs in a certain range,the upper-level is programmed to seek the comprehensive minimum value between system total travel time and the sensitivity of system total travel time under the limited funds constraint,and the lower-level programming is a stochastic user equilibrium assignment model.The sensitivity calculation formula of system total travel time travel demand was derived,and the solution algorithm of the model was also presented.At last,taking a test road network as an example,three decision optimization models based on system total travel time,based on sensitivity and based on comprehensive travel time with system total travel time and sensitivity were analyzed.The results show that three decision optimization models can seek the optimal urban road construction sequence of its objective function respectively,but the optimal results based on sensitivity and based on comprehensive travel time are more robust than the optimal result based on system total travel time under demand uncertainty.
作者
伍建辉
黄中祥
李武
吴健辉
彭鑫
张生
WU Jian-hui;HUANG Zhong-xiang;LI Wu;WU Jian-hui;PENG Xin;ZHANG Sheng(School of Traffic and Transportation Engineering,Changsha University of Science and Technology,Changsha 410114,China;College of Information&Communication Engineering,Hunan Institute of Science and Technology,Yueyang,Hunan 414006,China)
出处
《计算机科学》
CSCD
北大核心
2018年第4期89-93,共5页
Computer Science
基金
国家自然科学基金项目(51338002
51408058)
湖南省科技计划项目(2016TP1021)资助
关键词
交通网络设计
需求不确定
鲁棒优化
灵敏度
Traffic network design
Demand uncertainty
Robust optimization
Sensitivity