A time-dependent attention convolutional LSTM method for traffic flow prediction

Abstract

With traffic network becoming increasingly complicated, traffic flow prediction has important practical significance for the management of traffic roads and public safety. For example, an accurate taxi demand prediction can help to improve efficiency of vehicle scheduling and reduce traffic congestion. The main issue of flow prediction is how to extract the information of complex spatio-temporal dependencies and interactions between arrival and departure. To solve these problems, we develop a deep learning method based on time-dependent attention convolutional LSTM (TDAConvLSTM) in which a time-dependent attention mechanism is designed to learn similarities of historical traffic flows among different time intervals and a fusion mechanism is introduced to aggregate the feature information produced by convolutional LSTM and attention module. And then, the result of the feature aggregation is fed to a multi-layer deconvolutional network to gain the results of flow prediction. Experimental studies on two real-life datasets indicate that TDAConvLSTM achieves better results than the compared models. The source code of our proposed method is available at the URL1.