基于神经网络的路口交通流转向比预测

为了预测路口交通信号控制所需的转向交通流量,提出了基于改进BP(back-propagation)神经网络的路口交通流转向比预测模型,给出了相应参数的计算方法;采用自适应学习率和动量梯度下降法以提高神经网络的学习速度和算法的可靠性,并用调查数据对模型进行了检验.研究结果表明,与传统的平均值法相比,用所提出的模型,平均绝对相对误差减小约1%~3%.

ANN-Based Prediction of Turning Rate of Traffic Flows at Intersection

Based on an improved back-propagation neural network,a predication model for the turning rate of traffic flows at intersections was proposed to predict traffic flows for the signal control of intersections.The corresponding method to determine necessary parameters in this model was given.To improve the learning rate and reliability of neural network algorithms,the self-adaptive learning rate approach and the gradient descent with momentum method were adopted.In addition,a simulation was carried out to prove the correctness of the proposed model.The research result shows that compared with the average value method,the proposed model can decrease the mean absolute relative error by 1%~3%.