一种融合时空关联与社会事件的交通流预测方法

交通流预测作为智能交通系统的一个关键问题,是国内外交通领域的研究热点。交通流预测的主要挑战在于交通流数据本身具有复杂的时空关联,且易受各种社会事件的影响。针对这些挑战,提出一种用于交通流预测的深度学习框架。一方面,针对道路网络非欧氏的空间关联以及交通流时序数据的时间关联,设计了一种融合图卷积神经网络和循环神经网络的特征抽取子网络;另一方面,针对社会事件对交通流的潜在影响,设计了一种基于卷积神经网络的社会事件特征抽取子网络。最后,融合时空关联特征抽取子网络和社会事件特征抽取子网络,实现交通流预测模型。为了验证模型的有效性,文中基于真实交通流数据进行了实验。结果表明,所提模型与传统的预测模型相比具有较高的准确度,准确度提高了3%~6%。

Traffic Flow Forecasting Method Combining Spatio-Temporal Correlations and Social Events

Traffic flow prediction,as a key issue in intelligent transportation system,becomes a research hotspot in the field of transportation both at home and abroad.The main challenge of traffic flow prediction is twofold.First,traffic flow has complica-ted spatial and temporal correlations.Second,traffic flow can be influenced by social events.Aiming at these challenges,this paper proposes a deep learning framework for traffic flow prediction.On the one hand,a sub-network by combining graph convolutional neural network and recurrent neural network is designed to extract spatio-temporal correlation features from the non-European road network space.On the other hand,a sub-network based on convolutional neural network is designed to extract social event features from textual data.Finally,the traffic flow prediction model is implemented by merging the spatio-temporal correlation feature extraction subnetwork and the social event feature extraction sub-network.In order to verify the validity of the model,experiments are conducted based on real traffic flow data.Compared with the baseline methods,the proposed method has higher accuracy,and the accuracy improves by 3% to 6%.