摘要
次优拥挤收费问题一般要考虑不同决策者的不同利益,因此,有必要考虑多个收费策略建立多目标模型来均衡不同决策者的利益.由于决策者常在信息不确定的情况下做决策,在出行需求不确定的条件下,为了确定次优拥挤收费的方案,建立了基于条件风险价值的随机多目标双层规划模型,上层规划的目标函数考虑了系统总阻抗和社会公平性,下层规划是UE用户均衡配流问题.利用基于随机模拟的遗传算法对模型进行求解,并通过数值算例对模型和算法进行分析,验证了模型的有效性.
The second-best congestion pricing always involves a number of stakeholders with different needs, so it＇s necessary to consider multi-objective. In addition, the decisionmaking process sometimes has to be made under uncertainty where certain inputs are not known exactly. In this paper, we consider demand uncertainty and develop a stochastic multi-objective bi-level model. We consider two objectives, namely, t＇he total travel time and equity, and formulate conditional value-at-risk model. We solve this problem with a simulation-based genetic algorithm. A numerical example is also presented to illustrate the ＂model and algorithm.
出处
《数学的实践与认识》
北大核心
2015年第7期54-61,共8页
Mathematics in Practice and Theory
关键词
城市交通
拥挤收费
需求不确定
多目标规划
条件风险价值
urban traffic
congestion pricing
demand Uncertainty
multi-objective
condi-tional value-at-risk
