Research on a short-term traffic flow prediction method

Jun Nie; Weiqing Xie; Hang Zhang; Wen Zhang

doi:10.1117/12.3009524

16 October 2023 Research on a short-term traffic flow prediction method

Jun Nie, Weiqing Xie, Hang Zhang, Wen Zhang

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128030Y (2023) https://doi.org/10.1117/12.3009524
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Traffic flow prediction has a good guiding effect on traffic control. In response to the current inability of road traffic flow prediction methods to fully reveal the inherent laws of traffic flow, and considering the issue of fully considering spatiotemporal correlation in traffic flow prediction, this paper proposes an LSTM (Long Short-Term Memory) model based on Bayesian optimization. Experimental studies have shown that the LSTM model based on Bayesian optimization has good performance and high prediction accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Nie, Weiqing Xie, Hang Zhang, and Wen Zhang "Research on a short-term traffic flow prediction method", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128030Y (16 October 2023); https://doi.org/10.1117/12.3009524

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