Construction of a Short-Term Traffic Flow Prediction Model Based on Improved LSTM and Performance Evaluation Across Multiple Time Granularities

Nuo Chen

doi:10.54254/2755-2721/2025.BJ24681

Applied and Computational EngineeringOpen access

Construction of a Short-Term Traffic Flow Prediction Model Based on Improved LSTM and Performance Evaluation Across Multiple Time Granularities

Research Article

Open Access

Construction of a Short-Term Traffic Flow Prediction Model Based on Improved LSTM and Performance Evaluation Across Multiple Time Granularities

Nuo Chen ^1*

¹ Tiangong University, Tianjin, China

^*Corresponding author: C181557911N@126.com

Published on 4 July 2025

ACE Vol.177

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-241-6

ISBN (Online): 978-1-80590-242-3

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Abstract

As urban traffic congestion continues to intensify, predicting short-term traffic flow has become essential to enabling real-time control in intelligent transportation systems (ITS). However, traditional models face significant limitations in capturing the spatiotemporal and nonlinear characteristics of traffic data. Long Short-Term Memory (LSTM) networks, with their gated mechanisms, can effectively model long-term dependencies and periodic patterns in traffic flow. The accuracy of these predictions directly influences decision-making in scenarios such as traffic guidance and emergency management, offering substantial practical value for improving road network efficiency. This study constructs an optimized LSTM model to evaluate its effectiveness in short-term traffic prediction and to compare predictive performance across different time granularities. A dual-layer LSTM architecture is employed, incorporating the Adam optimizer, Dropout, and early stopping as regularization strategies. Using urban traffic monitoring data from the United States, both hourly and daily prediction models are developed for experimental validation. Results indicate that the hourly prediction model (MSE = 0.0709) markedly surpasses the daily model (MSE = 0.2987), effectively identifying recurring patterns like rush periods in the morning and evening. These outcomes offer a practical solution for adaptive traffic regulation.

Keywords:

Intelligent Transportation Systems, Short-Term Traffic Flow Prediction, LSTM, Deep Learning

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References

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References

[1]. Siddiquee, M. S. A., & Hoque, S. (2017). Predicting the daily traffic volume from hourly traffic data using artificial neural network. Neural Network World, (3).

[5]. Luo, X., Li, D., Yang, Y., & Zhang, S. (2018). Short-term traffic flow prediction based on KNN-LSTM. Journal of Beijing University of Technology, 44(12), 1521-1527.

[6]. Wei, W., Wu, H., & Ma, H. (2019). An autoencoder and LSTM-based traffic flow prediction method. Sensors, 19(13), 2946.

[7]. Wen, H., Zhang, D., & Lu, S. (2019). Application of the GA-LSTM model in highway traffic flow prediction. Journal of Harbin Institute of Technology, 51(09), 81-87+95.

[9]. Yang, B., Sun, S., Li, J., Lin, X., & Tian, Y. (2019). Traffic flow prediction using LSTM with feature enhancement. Neurocomputing, 332, 320-327.

[11]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

Cite this article

Chen,N. (2025). Construction of a Short-Term Traffic Flow Prediction Model Based on Improved LSTM and Performance Evaluation Across Multiple Time Granularities. Applied and Computational Engineering,177,1-9.

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-MLA 2025 Symposium: Applied Artificial Intelligence Research

ISBN: 978-1-80590-241-6(Print) / 978-1-80590-242-3(Online)

Editor: Hisham AbouGrad

Conference website: https://2025.confmla.org/

Conference date: 3 September 2025

Series: Applied and Computational Engineering

Volume number: Vol.177

ISSN: 2755-2721(Print) / 2755-273X(Online)

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