Abstract：In recent years, the traffic data has been increasing explosively. Accurate and timely prediction of traffic flow information is very important for intelligent transportation system. Based on LSTM neural network, a prediction method of short-time traffic flow considering the characteristics of passenger car proportion was proposed. The characteristics of passenger car proportion in the traffic flow data were extracted and the power spectrum was drawn by using the fast Fourier algorithm (FFT). The periodicity of the characteristics of passenger car proportion on expressway was verified. According to this, a prediction model of short-term traffic flow based on LSTM with the consideration of the characteristics of passenger car proportion was proposed, and an example of a toll station in Beihuan of Guangzhou was analyzed. The results show that the LSTM prediction model with the characteristics of passenger car proportion can effectively reduce the error of short-term traffic flow prediction and improve the accuracy of prediction.
翁小雄，郝翊. 基于LSTM引入客车占比特征的短时交通流预测[J]. 重庆交通大学学报（自然科学版）, 2020, 39(11): 20-25.
WENG Xiaoxiong，HAO Yi. Short-Term Traffic Flow Prediction Based on LSTM Algorithm with the
Characteristics of Passenger Car Proportion. Journal of Chongqing Jiaotong University(Natural Science), 2020, 39(11): 20-25.
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2020, Vol. 39 
