基于DWT-GCN的短时交通流预测

交通流预测是智慧交通领域的研究热点之一, 为了深层次地挖掘交通流序列的时空特征, 提高预测精度, 提出了一种基于离散小波变换(discrete wavelet transformation, DWT)和图卷积网络(graph convolutional network, GCN)短时交通流预测模型. 首先, 利用DWT算法将原始交通序列分解为细节分量与近似分量, 降低交通流数据的非平稳性; 其次, 引入距离因子项优化GCN模型中的邻接矩阵, 进一步提取路网的空间特征; 最后, 将DWT分解的各组分量数据分别作为GCN模型的输入进行预测, 并对各组预测结果进行重构, 得到最终预测值. 利用美国加利福尼亚州交通局PeMS数据库实测交通数据对模型进行测试, 结果表明, 该模型相比于ARIMA、WNN、GCN, 平均绝对误差平均降低57%, 平均绝对百分比误差平均降低59%, 是一种有效的短时交通流预测方法.

Short-term Traffic Flow Prediction Based on DWT-GCN

Traffic prediction is one of the research focuses in intelligent transportation. This study proposes a short-term traffic flow prediction model based on discrete wavelet transformation (DWT) and graph convolutional networks (GCNs) to deeply explore the temporal and spatial features of traffic flow sequences and improve prediction accuracy. Firstly, the original traffic sequences are decomposed into detailed and approximate components by the DWT algorithm to reduce the non-stationarity of traffic flow data. Secondly, the adjacency matrix of the GCN model is optimized by introducing the distance factor term to extract the spatial features of road networks. Finally, each group of components decomposed by DWT is used as the input of the GCN model separately for prediction, and the prediction results of each group are reconstructed to obtain the final prediction value. The model is tested on the Caltrans PeMS dataset, and the test results reveal that compared with the ARIMA, WNN, and GCN model, the proposed model has reduced its mean absolute error (MAE) and mean absolute percentage error (MAPE) by 57% and 59%, respectively, which proves to be an effective method for predicting short-term traffic flow.