期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 6, 页码 5615-5624出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3055258
关键词
Time series analysis; Predictive models; Fractals; Data models; Correlation; Biological neural networks; Training; Traffic engineering; short-term traffic flow prediction; LSTM_BILSTM method; time series analysis; urban road section
资金
- Natural Science Foundation of China [52062027, 52002282, 71861023]
- Natural Science Foundation of Zhejiang Province [LQ19E080003]
- Philosophy and Social Science Foundation of Zhejiang Province [21NDJC163YB]
- Philosophy and Social Science Program of Ningbo [G20-ZX37]
- Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University
This paper proposes a short-term traffic flow prediction model based on traffic flow time series analysis and an improved long short-term memory network. By integrating bidirectional long-term memory network, the model shows better accuracy and stability compared to other models.
The real-time performance and accuracy of traffic flow prediction directly affect the efficiency of traffic flow guidance systems, and traffic flow prediction is a hotspot in the field of intelligent transportation. To further improve the accuracy of short-term traffic flow prediction, a short-term traffic flow prediction model based on traffic flow time series analysis, and an improved long short-term memory network (LSTM) is proposed. First, perform time series analysis on traffic flow data and perform smoothing and standardization processing to obtain a stable time series as model input data, which can improve the accuracy of model training and eliminate the impact of a wide range of feature values. Then, an improved LSTM model based on LSTM and bidirectional LSTM networks are established. Combining the advantages of sequential data and the long-term dependence of forwarding LSTM and reverse LSTM, the bidirectional long-term memory network (BILSTM) is integrated into the prediction model. The first layer of the LSTM network learns and predicts the input time series and further learns and trains through the bidirectional LSTM network to effectively overcome the large prediction errors. Finally, the performance of the proposed method is evaluated by comparing the predicted results with actual traffic data. The model that is proposed in this paper is compared with the long short-term memory network (LSTM) model and the bidirectional long-term memory network (BILSTM) model. The results demonstrate that the proposed method outperforms both compared methods in terms of accuracy and stability.
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