基于改进eRCNN的局部路网交通流预测

针对误差反馈循环卷积神经网络在运用到短时交通流预测时存在仅仅能接收时序误差序列，忽略交通流误差数据中隐含的空间拓扑特征，且在模型初始化时其采用的通用卷积神经网络初始化方法降低了模型训练效率的问题，本文提出一种优化的误差反馈循环卷积神经网络模型，在误差反馈循环卷积神经网络模型基础上根据预测误差数据的时空特性对误差反馈层进行结构强化，能够处理包含简单空间关系的误差序列。同时通过在模型训练的过程中分离模型产生的历史预测误差和训练误差，使得模型构建过程更加高效，加速了模型收敛速度。通过北京市四环道路交通数据的实验表明，优化的误差反馈循环卷积神经网络预测模型在预测精度、构建效率及鲁棒性上均得到有效提高。

Local Road Network Traffic Flow Prediction Based on Improved eRCNN

Error feedback recurrent convolutional neural network can only handle time series error sequences when it is applied to short-time traffic flow forecasting. The spatial topological characteristics implied in traffic flow error data are ignored. Furthermore, at model initialization time, it uses the general convolutional neural network initialization method to reduce the training efficiency of the model. Aiming at these problems, this paper presents an optimal error feedback recurrent convolutional neural network model. The error feedback layer structure in the model is strengthened according to the spatial and temporal characteristics of prediction error data.The model can deal with the error sequence with simple spatial relation. Through separating the history prediction error and training error, the training strategy is also improved to accelerate the convergence speed of the model. Experiments on traffic data of Beijing fourth ring road show that the optimized error feedback recurrent convolution neural network forecasting model can effectively improve the prediction accuracy, construction efficiency and robustness.