结合变种残差模型和Transformer的城市公路短时交通流预测

Short-term traffic flow prediction of urban highway based on variant residual model and Transformer

城市公路交通流的预测受到历史交通流量和相邻车道交通流量的影响,蕴含了复杂的时空特征。针对传统交通流预测模型卷积长短时记忆(ConvLSTM)网络进行交通流预测时,未将时空特征分开提取而造成的特征提取不充分、特征信息混淆和特征信息缺失等问题,对ConvLSTM模型作出改进。首先,提取每个采样时刻的交通流数据的短期时间特征和空间特征,并在特定的维度下将交通流的短期时空特征融合;其次,进行残差映射;最后,将映射后的短期时空特征交由Transformer模型捕捉交通流数据长期的时空特征,并根据所捕捉的长期特征对未来时刻每个采样点交通流进行预测。使用加州城市快速路数据对模型进行验证,以平均绝对误差(MAE)作为模型评价指标时,所提模型相较于Conv-Transformer模型,预测精度提高了18%,验证了所提模型的有效性。

The prediction of urban highway traffic flow is influenced by historical traffic flow and neighboring lane traffic flow,involving complex spatio-temporal features.In order to address the insufficient feature extraction,feature confusion,and feature information loss caused by not separating the spatio-temporal features in the traditional traffic flow prediction model of Convolutional Long Short-Term Memory(ConvLSTM)network,some improvements were made to the ConvLSTM model.Firstly,the short-term temporal features and spatial features of the traffic flow data at each sampling moment were extracted,and the short-term spatio-temporal features of the traffic flow were fused in specific dimensions.Secondly,residual mapping was performed.Finally,the mapped short-term spatio-temporal features were input to the Transformer model to capture the long-term spatio-temporal features of the traffic flow data,based on which the traffic flow at each sampling point in the future moment was predicted.On California urban freeway data,with Mean Absolute Error(MAE)as the model evaluation metric,the proposed model has the prediction accuracy improved by 18%compared to the Conv-Transformer model,validating the effectiveness of the proposed model.