Bayesian network model for traffic flow estimation using prior link flows  

基于先验路段流的贝叶斯网络交通流量估计模型(英文)

作　　者：朱森来[1] 程琳[1] 褚昭明[1] 

机构地区：[1]东南大学交通学院,南京210096

出　　处：《Journal of Southeast University(English Edition)》2013年第3期322-327,共6页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.51078085,51178110)

摘　　要：In order to estimate traffic flow a Bayesian network BN model using prior link flows is proposed.This model sets link flows as parents of the origin-destination OD flows. Under normal distribution assumptions the model considers the level of total traffic flow the variability of link flows and the violation of the conservation law.Using prior link flows the prior distribution of all the variables is determined. By updating some observed link flows the posterior distribution is given.The variances of the posterior distribution normally decrease with the progressive update of the link flows. Based on the posterior distribution point estimations and the corresponding probability intervals are provided. To remove inconsistencies in OD matrices estimation and traffic assignment a combined BN and stochastic user equilibrium model is proposed in which the equilibrium solution is obtained through iterations.Results of the numerical example demonstrate the efficiency of the proposed BN model and the combined method.为了估计交通流量,提出了一个使用先验路段流的贝叶斯网络模型.该模型把路段流量设为OD流量的父节点.在正态分布假设下,模型考虑了总交通流水平、路段流可变性以及交通量守恒的随机扰动.根据先验路段流确定所有变量的先验分布.通过更新一些观测的路段流量,给出后验分布.后验分布的方差往往随着路段流量的逐步更新而不断减小.基于得到的后验分布,给出点预测和相应的概率区间.为消除OD矩阵估计和交通分配之间的不一致,组合了贝叶斯网络和随机用户均衡模型,通过迭代得到均衡解.算例结果验证了提出的贝叶斯网络模型和组合方法的效果.

关 键 词：traffic flow estimation Gaussian Bayesiannetwork evidence propagation combined method 

分 类 号：U412[交通运输工程—道路与铁道工程]