A Comparative Analysis of Machine Learning Regression Models for Urban Traffic Congestion Prediction

Kajal Singh; Ashish Chourey; Mohit Singh Tomar; Arun Kumar Jhapate

A Comparative Analysis of Machine Learning Regression Models for Urban Traffic Congestion Prediction

Author(s)	Ms. Kajal Singh, Dr. Ashish Chourey, Dr. Mohit Singh Tomar, Mr. Arun Kumar Jhapate
Country	India
Abstract	Traffic congestion has become a major challenge in urban transportation systems due to rapid urbanization and increasing vehicle ownership. This study proposes a machine learning regression framework for predicting traffic congestion in urban road networks using historical traffic data. Multiple regression algorithms, including Linear Regression, Decision Tree, Random Forest, Support Vector Regression (SVR), Gradient Boosting, and XGBoost, are evaluated to identify the most effective prediction model. The proposed methodology incorporates data preprocessing, feature selection, model training, and comparative performance analysis. Model performance is assessed using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²). Experimental results demonstrate that ensemble regression models outperform conventional regression techniques in terms of prediction accuracy and robustness. The findings indicate that XGBoost provides the best overall performance while maintaining computational efficiency. The proposed framework supports intelligent transportation systems by enabling accurate and timely traffic congestion prediction. Furthermore, the framework can assist transportation authorities in improving traffic management and reducing congestion-related problems. Future work will focus on integrating real-time IoT data and explainable artificial intelligence techniques to further enhance prediction accuracy and practical deployment.
Keywords	Traffic Congestion Prediction, Machine Learning, Regression Models, Urban Road Networks, Intelligent Transportation Systems, Traffic Flow Forecasting, XGBoost, Random Forest, Smart Cities, Data Analytics.
Field	Computer > Data / Information
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-21

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 4 Cover Page Vol 8 Isu 3 Cover Page Vol 8 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

A Comparative Analysis of Machine Learning Regression Models for Urban Traffic Congestion Prediction

Share this