MLSTAM: A multidimensional long-term spatio-temporal attention model for traffic flow forecasting by capturing time series correlations

Abstract

Traffic flow prediction occupies a pivotal position in intelligent transportation systems, and accurate traffic flow prediction is of great significance for alleviating traffic congestion and reducing the incidence of traffic accidents. To improve the accuracy of traffic flow forecasts, it is necessary to consider the historical data over a longer period. However, most of the existing methods only consider part of the recent historical time information, ignoring the implied fluctuation of the traffic flow in some regions in the historical contemporaneous time interval. Therefore, we propose a multidimensional long-term spatio-temporal attention model for traffic flow forecasting by capturing time series correlations. In this model, we design a multi-temporal dimensional attention mechanism and a deep fusion extraction convolutional neural network to capture multidimensional temporal information and fuse spatio-temporal correlations to predict traffic flow. The experimental results on two real datasets show that the proposed model outperforms the compared models.