基于粗糙神经网络的交通优化控制模型

在运用粗糙集理论对路网节点所测得的历史交通流量进行量化分析的基础上,基于神经网络自学习的能力,研究了实时动态交通流的模型结构并给出了交通流优化控制方法。首先,针对交通流优化控制的影响因素过于庞大的问题,采用粗糙集理论对其进行量化分析,建立了规则简化的数据清洗模型;然后,在此基础上利用以新的流量时间序列和原来的流量时间序列分别作为模型的输入和输出,构造出交通流量预测的人工神经网络模型并且加以训练;同时给出基于粗糙神经网络模型的交通流优化控制系统的运行机理并设计出相应的网络拓扑模型;最后,用某交通观测站的实际网络流量对该模型进行验证。仿真结果表明,该模型具有较好地预测效果。

Traffic-flow optimal control model in intelligent transport systems:rough neural network approach

Based on the Similarity principle of the fractal theory and on the quantitative analysis of the historical traffic flow measured on path network nodes through rough set theory,this paper discussed the mode structure of the real-time dynamic traffic flow and put forward the optimal control method.First of all,the paper established a data cleaning mode on the basis of the rough set theory;then analyzed the parameter structure in detail,established a forecast mode of the traffic flow with the similarity principle of the fractal theory,described the functioning principle of the optimal control system of the traffic flow and the corresponding network topology structure.Finally,the paper tested the mode with the real data measured from a traffic observation station,the result shows that the mode and the algorithm designed is actually effective.