一种基于序列到序列时空注意力学习的交通流预测模型

A Sequence-to-Sequence Spatial-Temporal Attention Learning Model for Urban Traffic Flow Prediction

城市交通流预测是研究交通时空序列数据的动态演化并预测未来交通情况的关键技术,对于智能交通预警及管理决策来讲至关重要.但是有效的交通流建模非常具有挑战性,因为它受到很多复杂因素的影响,例如交通网络的时空依赖性和序列突变性等问题.一些研究工作将卷积神经网络(convolutional neural networks, CNN)或循环神经网络(recurrent neural networks, RNN)用于交通流量预测建模.但是,直接使用经典的深度学习模型难以有效捕获与交通流相关的多通道多变量序列数据中的隐含时空依赖性特征.针对上述问题,提出了一种新的序列到序列时空注意力深度学习框架(spatial-temporal attention traffic forecasting, STATF)来处理城市交通流建模任务,它是一种基于卷积LSTM编码层和LSTM解码层,并辅助注意力机制的端到端深度学习模型,可以自适应地学习与城市交通流相关的多通道多变量时空序列数据中的时空依赖性和非线性相关性特征.基于3个真实的交通流数据集实验结果表明:不管是单步预测还是多步预测条件下,STATF模型都具有更优的预测性能.

Urban traffic flow prediction is a key technology to study the behavior of traffic-related big data and predict future traffic flow, which is crucial to guide the early warning of traffic congestion in the intelligent transportation system. But effective traffic flow prediction is very challenging as it is affected by many complex factors, e.g. spatial-temporal dependency and temporal dynamics of traffic networks. In the literature, some research works applied convolutional neural networks(CNN) or recurrent neural networks(RNN) for traffic flow prediction. However, it is difficult for these models to capture the spatial-temporal correlation features of traffic flow related temporal data. In this paper, we propose a novel sequence-to-sequence spatial-temporal attention framework to deal with the urban traffic flow forecasting task. It is an end-to-end deep learning model which is based on convolutional LSTM layers and LSTM layers with attention mechanism to adaptively learn spatial-temporal dependency and non-linear correlation features of urban traffic flow related multivariate sequence data. Extensive experimental results based on three real-world traffic flow datasets show that our model has the best forecasting performance compared with state-of-the-art methods.