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Traffic index prediction based on sequence to sequence learning

The traffic index prediction plays an important role in many intelligent transportation applications. The challenge mainly comes from the strong nonlinearities in the changing of traffic index, which is caused by transitions of various traffic states including smooth traffic, congestion, breakdown and recovery. Deep learning performs well in capturing and describing the nonlinear relationship among variables. This paper uses a Sequence to Sequence (Seq2Seq) deep learning framework to predict the traffic index. In the proposed method, we use Long Short Term Memory (LSTM) as the basic circulating unit. And the LSTM units are stacked to extract the time changing characteristic and the periodic characteristic of the input traffic index sequence. Besides, the extracted feature vectors are decoded by another single layer LSTM to predict the traffic index of each time after inputting sequence. Experiments are conducted on Beijing traffic index datasets. The proposed method outperformed Autoregressive Integrated Moving Average Model (ARIMA) and LSTM under some commonly used evaluation metrics.