A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting

Short-term traffic flow forecasting at isolated points is a fundamental yet challenging task in many intelligent transportation systems. We present a novel long short-term memory (LSTM) network enhanced by temporal-aware convolutional context (TCC) blocks and a new loss-switch mechanism (LSM) to carry out this task. Compared with conventional recurrent neural networks (RNN) or LSTM networks, the proposed network can capture much more distinguishable temporal features and effectively counteracting noise and outliers for more accurate prediction. The proposed TCC blocks, leveraging dilated convolution, produce an enlarged receptive field in temporal contexts, and formulate a temporal-aware attention mechanism to learn the complicated and subtle temporal features from the traffic flows. We further cascade multiple TCC blocks in the network to learn more temporal features at different scales. To deal with the noise and outliers, we propose a novel loss-switch mechanism (LSM) by combining the traditional mean square error loss and the generalized correntropy induced metric (GCIM), which is capable of effectively counteracting non-Gaussian disturbances. The whole network is trained in an end-to-end manner guided by the loss-switch mechanism. Extensive experiments are conducted on two typical benchmark datasets and the experimental results corroborate the superiority of the proposed model over state-of-the-art methods.