基于改进时空残差卷积神经网络的城市路网短时交通流预测

城市路网交通流预测受到历史交通流和相邻路口交通流的影响，具有复杂的时空关联性。针对传统时空残差模型缺乏对交通流数据进行相关性分析、捕获微小变化而容易忽略长期时间特征等问题，提出一种基于改进时空残差卷积神经网络（CNN）的城市路网短时交通流预测模型。该模型将原始交通流数据转化成交通栅格数据，利用皮尔逊相关系数（PCC）对交通栅格数据进行相关性分析，确定相关性高的周期序列和邻近序列；同时，建立周期序列模型和邻近序列模型，并引入长短时记忆（LSTM）网络作为混合模型提取时间特征以及捕获两种序列的长期时间特征。利用成都市出租车数据集对模型进行验证，结果表明该模型预测结果优于LSTM、CNN和传统残差模型等基准模型，以均方根误差（RMSE）为评价指标时，所提模型将测试集中交通路网的平均预测精度分别提高了25.6%、13.3%和3.2%。

Improved spatio-temporal residual convolutional neural network for urban road network short-term traffic flow prediction

Traffic flow prediction for urban road network is influenced by historical traffic flow and traffic flow at adjacent intersections， which has complex spatio-temporal correlation. For the lack of correlation analysis on traffic flow data， capturing small changes but ignoring long-term time characteristics in traditional spatio-temporal residual models， a short-term traffic flow prediction model for urban road network based on improved spatio-temporal residual Convolutional Neural Network （CNN） was proposed. In the proposed model， the original traffic flow data was transformed into traffic grid data， and Pearson Correlation Coefficient （PCC） was used to analyze the correlation of traffic grid data， so as to determine the periodic series and adjacent series with high correlation. At the same time， the periodic series model and the adjacent series model were established， and Long Short-Term Memory （LSTM） network was introduced as the hybrid model to extract the time characteristics and capture the long-term time characteristics of the two series. Experimental results on Chengdu taxi dataset show that the proposed model can predict traffic flow better than benchmark models of LSTM， CNN and the traditional residual model. When the evaluation index is Root Mean Square Error （RMSE）， the average prediction accuracy of traffic road network in the test set is improved by 25.6%， 13.3% and 3.2% respectively.