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Application effect of short-term traffic flow prediction method based on CNNBLSTM algorithm.

Guozhu Sui¹, Meixia Song¹, Ke Bian¹

¹School of Traffic and Electrical Engineering, Dalian University of Science and Technology, Dalian, China.

|July 7, 2025

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This study introduces an improved deep learning model for accurate short-term traffic flow prediction. The enhanced algorithm significantly boosts prediction precision and convergence speed, offering a robust solution for traffic management.

Area of Science:

Artificial Intelligence
Transportation Engineering
Data Science

Background:

Traditional traffic flow prediction algorithms struggle with spatiotemporal dynamics, leading to reduced accuracy.
Accurate short-term traffic flow forecasting is crucial for effective traffic management and planning.

Purpose of the Study:

To develop an advanced short-term traffic flow prediction method.
To enhance prediction accuracy and model convergence speed using deep learning techniques.

Main Methods:

Data preprocessing involved identifying, repairing, and decomposing abnormal traffic flow data using smoothing estimation thresholds and adaptive noise integration empirical modal decomposition.
A hybrid deep learning model combining an improved convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) algorithm was proposed.

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Enhanced Adam and Lookahead optimization algorithms were integrated to improve model performance.

Main Results:

The proposed method demonstrated faster convergence and significantly lower training and validation loss values.
Training loss decreased from 0.0250 to 0.0021, and validation loss decreased from 0.0010 to 0.0008.
The model achieved superior prediction accuracy compared to traditional CNN-BiLSTM models, with an average absolute percentage error of 0.233 and a root mean square error of 23.87.

Conclusions:

The developed algorithm effectively and precisely forecasts short-term traffic flow.
The enhanced deep learning approach provides a reliable foundation for intelligent transportation systems and traffic management decisions.
The study highlights the potential of integrating CNN, BiLSTM, and advanced optimization techniques for complex time-series prediction tasks.