Exploring Deep Learning Applications in Traffic Flow Prediction

Huayi Guo

doi:10.54254/2753-8818/2025.DL27110

Theoretical and Natural ScienceOpen access

Exploring Deep Learning Applications in Traffic Flow Prediction

Research Article

Open Access

Exploring Deep Learning Applications in Traffic Flow Prediction

Huayi Guo ^1*

¹ Nanjing University of Information Science and Technology

^*Corresponding author: 202283710066@nuist.edu.cn

Published on 24 September 2025

TNS Vol.132

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-305-5

ISBN (Online): 978-1-80590-306-2

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Abstract

Traffic flow prediction plays an important role in intelligent transportation systems, supporting efficient traffic management and scientific decision-making. With the rapid advancement of deep learning, various advanced models have sprung up, aiming to capture the nonlinear, high-dimensional and dynamic evolution of traffic data. This paper systematically reviews three representative methods: autoencoder-based models, recurrent neural networks (RNNs), and graph neural networks (GNNs). With its stack-optimized structure, Autoencoder has demonstrated its capability in feature extraction, noise reduction and efficient representation learning, thereby improving prediction robustness and computational efficiency. RNNs and their variants (including LSTM and GRU) excel at portraying the temporal dependence of traffic sequences, while hybrid models that incorporate external data or optimize algorithms further enhance accuracy and adaptability. In recent years, GNN methods have emerged to specialize in spatial dependencies caused by complex road network structures. By combining GCNs with temporal models and enhanced learning, these methods have achieved remarkable results in capturing spatio-temporal correlations and long-term predictions. Overall, the integrated evolution of AE, RNN and GNN models explains the leap-forward development from feature compression, temporal modeling to spatio-temporal integrated learning. Despite the significant advantages, challenges remain in multimodal data fusion, computing efficiency and real-time deployment, which points out a promising direction for future research.

Keywords:

Traffic Flow Prediction, Autoencoder (AE), Recurrent Neural Network (RNN), Graph Neural Network (GNN), Spatiotemporal Modeling.

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References

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References

[1]. Jin, Y., Xu, W., Wang, P., & Yan, J. (2018, August). SAE Network: A deep learning method for traffic flow prediction. IEEE Xplore.

[5]. Tian, Z., & He, D. (2022, April). Short-term traffic flow prediction based on GMO-DELM. In 2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) (pp. 260–264).

[8]. He, L., Shi, S., Zhang, D., & Luo, W. (2025, July). ST-RLNet: Spatio-temporal representation learning for multi-step traffic flow prediction. Neurocomputing, 652, 131020.

Cite this article

Guo,H. (2025). Exploring Deep Learning Applications in Traffic Flow Prediction. Theoretical and Natural Science,132,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-APMM 2025 Symposium: Simulation and Theory of Differential-Integral Equation in Applied Physics

ISBN: 978-1-80590-305-5(Print) / 978-1-80590-306-2(Online)

Editor: Marwan Omar, Shuxia Zhao

Conference website: https://www.confapmm.org/dalian.html

Conference date: 27 September 2025

Series: Theoretical and Natural Science

Volume number: Vol.132

ISSN: 2753-8818(Print) / 2753-8826(Online)

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