多属性随机时间依赖网络路径优化

Path Optimization in Stochastic and Time-Dependent Networks with Multi-attributes

为了改善实时交通诱导,根据交通网络中路段属性具有随机时间依赖性的特点,将路段各属性定义为关于时间的离散随机变量,建立了多属性条件下随机时间依赖网络路径优化模型.基于信息熵的多属性决策方法,设计了模型的求解算法;优化了网络节点的搜索顺序,使每个节点的信息只通过一步更新就可得到,从而获得多属性条件下所有节点到给定终点的路径选择结果,出行者可以根据到达某节点的具体时刻选择行进方向.最后,通过算例给出了算法的实现过程,并说明了随机时间依赖网络不遵从"先进先出(FIFO)"条件.

In order to improve the real-time traffic guidance,all the attributes for each section were defined as discrete random variables with respect to time in the light of the stochastic and time-dependent properties of attributes of sections in traffic networks.Based on the above,a model of path optimization was built for stochastic and time-dependent networks with multi-attributes.The multiple attribute decision making based on the information entropy theory was used to get the algorithm for the model.In this model,by adjusting the search order of nodes the information of each node can be obtained through only one computational step,the results of path selection can be achieved from all nodes to a specified destination in multi-attributes,then a traveler can get his ways by the arrival time.In the end,an example was given to illustrate the concrete steps of the algorithm and prove that stochastic and time-dependent networks do not follow ＂first in,first out＂ condition.