Advancing Traffic Flow Prediction: A Comprehensive Approach to Addressing Urban Congestion and Dynamic Transport Challenges

Jin  Wang; Minhui  Kang

doi:10.6180/jase.202606_29(6).0005

Advancing Traffic Flow Prediction: A Comprehensive Approach to Addressing Urban Congestion and Dynamic Transport Challenges

Computer Science and Information Engineering

Jin Wang¹This email address is being protected from spambots. You need JavaScript enabled to view it. and Minhui Kang²

¹School of Information Engineering, Jiangxi Vocational and Technical College of Communications, Nanchang 330013, Jiangxi, China

²School of Power Engineering, Jiangxi Vocational and Technical College of Electricity, Nanchang 330000, Jiangxi, China

Received: April 19, 2025
Accepted: August 24, 2025
Publication Date: October 19, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202606_29(6).0005

The sharp upsurge in car ownership has led to increased demands and congestion in urban transport networks. A dynamic traffic-flow forecast is crucial for managing demand and addressing capacity issues. Precise, real-time models of traffic flow help analyze its characteristics, predict trends, and provide logical insights to guide traffic management measures. This paper explores traffic flow prediction using the latest machine learning techniques, specifically employing CAT-Boost Regression (CATR), Random Forest Regression (RFR), and Gaussian Process Regression (GPR) as baseline predictive models. Additionally, the Artificial Rabbits Optimization (ARO) algorithm is introduced as an optimization strategy to enhance the accuracy of these models. The integration of ARO with the base models results in three hybrid models: CAAR, RFAR, and GPAR. Based on the predictive performance scores, the improved hybrid models with ARO optimization return higher scores, indicating that this approach is both efficient and potentially effective in handling traffic flow prediction issues. Among the models, the CAAR model performs best during the test phase, with the highest R² score of 0.980. In contrast, the GPR model shows the weakest performance, with an R² value of 0.891 . Furthermore, during the test phase, the CAAR model achieves the best performance according to the RMSE metric, with a value of 24.872, while the GPR model exhibits the lowest performance with an RMSE value of 60.971.

Keywords: Traffic Flow Prediction; Urban Transport; Hybrid Models; State-of-art algorithms

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