基于贝叶斯网络的城市交通规划辅助决策模型

Assistant decision-making model of urban traffic planning based on Bayesian network

为解决传统模型定位跟踪能力较差、交通规划决策线路覆盖不均衡的问题,构建基于贝叶斯网络的城市交通规划辅助决策模型。根据城市交通特征提取机动车的分布状态,得到城市某一随机路段的交通流量,利用贝叶斯网络定位跟踪交通移动目标,实现交通流量和通行状态的实时追踪、结合跟踪结果,依据平衡分配规划指标构建城市交通规划辅助决策模型。结果表明:所建模型在不同移动速度下的跟踪误差小于2组传统模型;在城市交通线路规划决策上,该模型反馈的交通线路覆盖更加均衡,能够为整个城市区域的交通线路提供更加多样化的选择,模型的综合性能更优。

In order to solve the problems of poor positioning and tracking ability of traditional models and unbalanced problems of route coverage in traffic planning decision-making,an auxiliary decision-making model of urban traffic planning based on Bayesian network is constructed.According to the characteristics of urban traffic,the distribution state of motor vehicles is extracted,and the traffic flow of a certain random road section in the city is obtained.The Bayesian network is used to locate and track the traffic moving target,so as to realize the real-time tracking of traffic flow and traffic state.Combined with the tracking results,the auxiliary decision-making model of urban traffic planning is established according to the balanced allocation planning index.The results show that the tracking error of the model under different moving speeds is less than that of the two traditional models;in the decision-making of urban traffic route planning,the traffic route coverage fed back by the model is more balanced,which can provide more diversified choices for the whole urban area,and the comprehensive performance of the model is better.