基于移动检测技术的城市路网拥挤预测模型

Urban Road Network Traffic Congestion Prediction Model Based on Probe Vehicle Technology

提出了一种新的基于移动检测技术、神经网络和模糊判断方法的城市路网动态交通拥挤预测模型。首先构建一个3层BP神经网络模型判断路网实时交通流状态,并应用实地移动检测数据和视频数据获取BP神经网络训练样本并对其进行训练;然后结合路网静态拓扑结构,应用多重模糊推理,对路段发生交通拥挤的发生可能性、拥挤程度和形成时间做出预测。现场实测数据表明,该模型具有良好的预测效果。

A model for urban road network traffic congestion forecast based on probe vehicle technology, fuzzy logic judgement and back-propagation （BP） neural network was proposed. A three-layer BP neural network model was built to estimate the real-time traffic flow of road network and to obtain BP neural network training specimen for the training by probe vehicle data and video data. Then the congestion posiblity, level of eogestion and the forming time of the link were estimated based on the road network topology and multiple fuzzy logic reasoning. The in-situ test shows good forecast result by the model.