Singular Point Probability Improve LSTM Network Performance for Long-term Traffic Flow Prediction

Traffic flow forecasting is the key in intelligent transportation system, but the current traffic flow forecasting method has low accuracy and poor stability in the long-term period. For this reason, an improved LSTM Network is proposed. Firstly, the concept and calculation method of time singularity ratio of traffic data stream is proposed to predict long-term traffic flow. The singular point probability LSTM (SPP-LSTM) is presented. Namely, the algorithm discard the LSTM network unit form the network temporarily according to the singular point probability during the training process of the depth learning network, so as to get SPP-LSTM model. Finally, the paper amends the SPP-LSTM by ARIMA to realize the accurate prediction of 24-hour traffic flow data. Theoretical analysis and experimental results show that the SPP-LSTM has a high accuracy rate, stability and wide application prospect in the long-time traffic flow forecast with hourly period.