城市道路交通拥堵的模糊神经网络评析

以城市道路网络的拥堵状况为研究对象,运用模糊层次分析以及神经网络理论构建城市道路交通拥堵的模糊神经网络评价模型.以层次分析为准则,从微观层、中观层、宏观层等方面建立道路交通拥堵三层评价指标体系;利用模糊一致性判别矩阵界定道路交通拥堵评价因子综合权重,基于BP神经网络构建了道路交通拥堵的模糊神经网络评价模型,并对道路交通拥堵评价区间进行阈值确定,将道路交通拥堵评价集界定为严重拥堵、中度拥堵、轻度拥堵、较为畅通、畅通等5个等级.以2016年上海市20个交通小区的道路交通拥堵数据为样本进行实例分析,结果表明该方法的可行性以及有效性. 

Fuzzy Evaluation of Urban Traffic Congestion Based on Neural Network

To solve the problem of the congestion of urban traffic network,a fuzzy evaluation model of urban road traffic congestion was developed based on the fuzzy analytic hierarchy process and neural network theory.Firstly,taking AHP as the criterion,a road traffic congestion evaluation index system was established with three levels,including microcosmic,meso and macro layer.Then a fuzzy consistent judgment matrix was used to define the evaluation factor weights of road traffic congestion,a fuzzy neural network evaluation model was built based on BP neural network to judge the road traffic congestion situation,the threshold intervals of the road traffic congestion evaluation were determined as five grades,including moderate congestion, severe congestion,mild congestion,relatively smooth and smooth.The traffic congestion data of 20 traffic districts in Shanghai in 2016 were taken as example to implement the method.The results show the feasibility and effectiveness of the method.