基于动态时空神经网络的城市交通流量预测方法

City Traffic Flow Prediction Method Based on Dynamic Spatio-Temporal Neural Network

交通流量预测对城市道路规划、交通安全问题和建设智慧城市等具有重要意义。然而,现有大部分交通预测模型无法很好地捕捉交通数据的动态时空相关性。针对该问题,提出了一种基于动态时空神经网络的城市交通流量预测方法。首先,通过对交通数据的最近周期依赖、日周期依赖和周周期依赖进行建模,在每个分量上使用三维卷积神经网络提取城市交通高维特征;然后,使用改进的残差结构捕捉远距离区域对与预测区域的相关度,融合空间注意力和时间注意力机制捕捉不同区域不同时间段上的交通流量之间的动态相关性;最后,使用基于参数矩阵的方法对3个分量的输出进行加权融合,得到预测结果。在TaxiBJ和BikeNYC两个公开数据集上开展实验,结果表明所提模型的预测性能优于主流交通预测模型。

Traffic flow forecasting is of great importance to urban road planning,traffic safety issues and building smart cities.However,most existing traffic prediction models cannot capture the dynamic spatio-temporal correlation of traffic data well enough to obtain satisfactory prediction results.To address this problem,a dynamic spatio-temporal neural network-based city traffic flow prediction method is proposed to solve the traffic flow prediction problem.First,by modelling the nearest cycle dependence,daily cycle dependence and weekly cycle dependence of the traffic data,a 3D convolutional neural network is used on each component to extract the high-dimensional features of urban traffic.Then,an improved residual structure is used to capture the correlation between remote area pairs and the prediction area,and a fusion of spatial attention and temporal attention mechanisms is used to capture the dynamic correlation between traffic flows in different time periods in different areas.Finally,the outputs of the three components are weighted and fused using a parameter matrix-based approach to obtain the prediction results.Experiments on two publicly available datasets,TaxiBJ and BikeNYC,show that the proposed model outperforms the mainstream traffic forecasting models.