Survey of short-term traffic flow prediction based on LSTM

Changxi Ma () and Tao Liu
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Changxi Ma: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, Lanzhou, P. R. China
Tao Liu: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, Lanzhou, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 02, 1-23

Abstract: Short-time traffic flow prediction can not only help urban traffic management to complete the control and induce but also reduce the degree of urban congestion and improve the efficiency of urban operation. At the same time, improving the effect of short-time traffic flow prediction is one of the key points of traffic control and guidance development. Currently, there are many different models for short-term traffic flow prediction, and different prediction models have their different advantages and disadvantages. In this paper, we review the current status of research on short-time traffic flow prediction based on Long Short-Term Memory (LSTM) neural network: first, we analyze the research method in this paper; second, we briefly review the research and application effects of common prediction methods in traffic flow prediction; finally, the optimization method based on LSTM and its application effect are summarized and analyzed from the aspect of LSTM network and its combination model. In addition, short-time traffic flow prediction still faces challenges due to the problems of the data itself and the complexity of real-world traffic flow impact factors.

Keywords: Long and short term memory; neural network; short-term traffic flow prediction; intelligent transportation system (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124501778

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