  In this paper, we consider the temporal pattern in traffic flow time series,
and implement a deep learning model for traffic flow prediction. Detrending
based methods decompose original flow series into trend and residual series, in
which trend describes the fixed temporal pattern in traffic flow and residual
series is used for prediction. Inspired by the detrending method, we propose
DeepTrend, a deep hierarchical neural network used for traffic flow prediction
which considers and extracts the time-variant trend. DeepTrend has two stacked
layers: extraction layer and prediction layer. Extraction layer, a fully
connected layer, is used to extract the time-variant trend in traffic flow by
feeding the original flow series concatenated with corresponding simple average
trend series. Prediction layer, an LSTM layer, is used to make flow prediction
by feeding the obtained trend from the output of extraction layer and
calculated residual series. To make the model more effective, DeepTrend needs
first pre-trained layer-by-layer and then fine-tuned in the entire network.
Experiments show that DeepTrend can noticeably boost the prediction performance
compared with some traditional prediction models and LSTM with detrending based
methods.
