摘要
鉴于Wardrop原理假设通道内的旅客对各种运输方式的实际出行费用能够完全准确估计的不足,利用不确定规划理论,结合不同出行距离、不同收入水平的旅客对运输方式服务属性的评价,用数学期望表示旅客出行的广义费用,提出了不确定条件下运输通道内各种运输方式旅客最优和运输系统最优客运量分担率计算模型,以及多目标客运量分担率计算模型,并设计了用于求解模型的基于随机模拟的遗传算法。客运量分担率的预测结果与实际测量值之间平均误差为8.13%,说明本模型能够有效地模拟旅客在出行时对运输方式选择的不确定性。
In order to solve the shortcoming of travel cost exact evaluation for passengers with Wardrop theory, uncertain programming theory was analyzed, the evaluations of different economy levels and different income leves passengers for transport modes were considered, the travel cost was expressed by mathematic expectation, three different computation models of passenger traffic sharing rates were set up, which were passenger equilibrium model, system optimization model and multi-object optimization model, a genetic algorithm based on stochastic simulation to calculate the models was put forward. error of computation values and measure values is uncertain of passenger choice transport modes. 5 tabs, Simulation result shows that the average 8. 13%, which effectively indicates the 9 refs.
出处
《交通运输工程学报》
EI
CSCD
北大核心
2005年第4期111-115,共5页
Journal of Traffic and Transportation Engineering
基金
国家西部交通建设科技项目(200131822336)
关键词
交通规划
客运量分担率
随机规划
Wardrop原理
遗传算法
traffic planning
passenger traffic sharing rates
stochastic optimization, Wardrop theory
genetic algorithm