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Title	Deep Learning With Bidirectional Long Short-Term Memory For Traffic Flow Prediction
ID_Doc	17924
Authors	Xue S.; Shao C.; Wang S.; Zhuang Y.
Year	2021
Published	Journal of Physics: Conference Series, 1972, 1
DOI	http://dx.doi.org/10.1088/1742-6596/1972/1/012098
Abstract	With the development of cities, the total number of trucks has increased year by year. Traffic flow forecasting has become an indispensable part of the cargo transportation industry and directly affects the development of the transportation industry. In the field of traffic flow prediction, Long Short-Term Memory(LSTM) model has advantages in processing time series, but it cannot extract the periodicity in time series. Therefore, in this experiment, a Bidirectional Long Short-Term Memory (BLSTM) model was constructed to predict traffic flow in the road network. It is worth mentioning that this article considers the non-parametric model autoregressive integrated moving average model (ARIMA) and the parametric model recurrent neural network (RNN) to compare and analyze with LSTM. Data from Guangwu Toll Station, Zhengzhou city, China were used to calibrate and evaluate the models. The experimental results show that the performance of RNN based on deep learning such as BLSTM and LSTM model is better than that of ARIMA. In order to better illustrate the advantages of BLSTM model, we comprehensively considered the performance effects of four models under morning peak, evening peak and flat peak. Experiments have proved that BLSTM has good nonlinear fitting ability and anti-noise ability, and the average prediction accuracy reaches 92.873%. © Published under licence by IOP Publishing Ltd.
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