次优拥挤收费的随机双目标模型

摘要

次优拥挤收费是缓解交通拥挤的一种有效手段,但可能产生不公平等问题,遭到大众反对.本文综合考虑系统总阻抗和公平性指标,将两者同时作为拥挤收费的目标函数,力图在缓解拥挤的同时,使得由于收费产生的不公平性最小.由于网络规划者有时候必须在不确定的环境下做出收费决策.本文将实际情况中出行需求的不确定性考虑到拥挤收费的决策过程,建立随机双目标的次优拥挤收费模型,以期得到的收费决策更加符合现实情况.对于这一模型,本文采用基于随机模拟的遗传算法进行求解.最后给出一个数值算例对所提出的模型和算法进行验证,说明综合考虑系统总阻抗和公平性指标两个目标,确实能够在缓解拥挤的同时,降低收费带来的不公平.%The second-best congestion pricing is an effective measure to alleviate congestion, but it often leads to inequity. In this paper, we consider both total travel time and equity as planner's congestion pricing objective function to formulate the model. In this way, we can minimize the inequity caused by congestion pricing as well as alleviating congestion. In addition, the decision-making process sometimes has to be made under uncertainty where certain inputs are not known exactly. In this paper we consider demand uncertainty and formulate second-best congestion pricing' s stochastic bi-objective model. A stochastic simulation-based genetic algorithm is used to solve the model. A numerical example is also presented to illustrate the model and algorithm.