基于神经网络的无信号路口人车交通行为预测分析

以无信号灯路口人车交通行为为研究对象,对行人和机动车辆在无信号灯路口的整体交通行为进行分类预测。在对路口现场交通情况进行拍摄后,用电脑的分帧技术对所需要的数据进行提取和分类,而后建立BP神经网络模型,确定神经网络的输入变量与输出变量。将样本数据导入神经网络并进行训练和测试后,得出行人和车辆过街类型的分类准确率,并且通过准确率所达到的标准来证明了BP神经网络模型的可行性。

Prediction and Analysis of Human and Vehicle Traffic Behavior at Signalless Intersection Based on Neural Network

Taking the pedestrian and vehicle traffic behavior at the intersection without signal light as the research object,the overall traffic behavior of pedestrians and motor vehicles at the intersection without signal light is classified and predicted. After photographing the traffic situation at the intersection,the required data is extracted and classified by computer framing technology,and then the BP neural network model is established to determine the input variables and output variables of the neural network. After the sample data is introduced into the neural network,trained and tested,the classification accuracy of pedestrian and vehicle crossing types is obtained,and the feasibility of BP neural network model is proved by the standard of accuracy.