Traffic flow prediction under multiple adverse weather based on self-attention mechanism and deep learning models

Abstract

To improve the accuracy of traffic flow prediction under adverse weather, a deep hybrid attention (DHA) model including the traffic and weather blocks is proposed. The traffic block introduces the convolutional neural network (CNN) and the gated recurrent unit (GRU) neural network to capture the spatio-temporal rules of the traffic flow data. To consider the impacts of adverse weather on traffic flow, the weather block is introduced. The weather block utilizes the convolutional long short-term memory (ConvLSTM) neural network to extract the relationship between the weather and traffic flow data. The self-attention mechanism is embedded in the two blocks. Four cases involving rainy, foggy and windy weather are used to verify the DHA model. The experiments reveal that: the DHA model shows the excellent performance under multiple adverse weather; different adverse weather has various impacts on the rules of traffic volume and speed; the prediction accuracy of each model reduces with the increase of the severe degree of each type of adverse weather.