Abstract
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.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Project no. 61762033, 61363071), The National Natural Science Foundation of Hainan (Project no. 617048), Hainan University Doctor Start Fund Project (Project no. kyqd1328). Hainan University Youth Fund Project (Project no. qnjj1444). State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University. College of Information Science and Technology, Hainan University. Nanjing University of Information Science and Technology (NUIST), A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology.
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Liu, B., Cheng, J., Cai, K., Shi, P., Tang, X. (2017). Singular Point Probability Improve LSTM Network Performance for Long-term Traffic Flow Prediction. In: Du, D., Li, L., Zhu, E., He, K. (eds) Theoretical Computer Science. NCTCS 2017. Communications in Computer and Information Science, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-10-6893-5_24
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DOI: https://doi.org/10.1007/978-981-10-6893-5_24
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