城市公交线网差异化计程票价多目标优化

Multi-objective Optimization of Urban Public Transportation Network Differentiated Fare

为获取更加接近实际城市公交线网的票价策略,将出行者的社会互动行为与后悔心理引入广义费用,提出线路客流OD矩阵均衡算法;分别以交通管理部门利润最大化及出行者效用最大化为目标,以公交计程票价、发车频率、私家车停车费为变量,建立固定需求下公交线网差异化计程票价多目标优化模型.引入集群智能多目标优化算法求解,并应用于Mandl标准公交线网.研究发现:以线路里程为标准,差异化计程票制可以有效降低出行成本;依据帕累托最优解调节票价,可以促进出行者选择行为向优势均衡转移.

This study proposes the equalization algorithm of passenger flow OD matrix to make the pricing strategy of urban public transport network be more effective. The multiplier effect of social interaction and regret psychology are introduced in travelers’ generalized cost analysis. The multi-objective optimization model is developed to reflect the public transport network’s differentiated fare under fixed demand. The objectives of the model are the maximum profit of operational department and maximum utility for travelers. The distance-based fare, private car parking fee, and the departure frequency of public transport are variables in the model. The multiobjective optimization algorithm based on cluster intelligence is introduced to solve the model, the proposed model and algorithm are applied to the standard Mandl network. The results indicate that the distance-based public transport fare can reduce travel cost, and adjusting the fare by pareto optimal solution can promote travelers’ choice behavior transfer to advantage equilibrium.