International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Urban Traffic Congestion Prediction Using GTFS Data and Advanced Machine Learning Models

Ali Atta GheniDepartment of Computer and Communications, Faculty of Engineering, Islamic University of Lebanon, Wardanieh, Lebanon

Vol 8 No 10 (2024): Volume 8, Issue 10, October 2024 | Pages: 25-31

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 08-10-2024

doi Logo doi.org/10.47001/IRJIET/2024.810005

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Abstract

Urban traffic congestion represents a complex challenge influenced by many dynamic factors. Peak periods typically exacerbate congestion, while bad weather can slow vehicle movements and increase travel times. Accidents and road closures cause sudden and unexpected disruptions, making traffic management a constant challenge. Using a dataset of over 66,000 GTFS records with machine learning classifiers like Random Forest, XGBoost, CatBoost, and Decision Tree models, the study seeks to forecast traffic conditions. SMOTE is used to ensure greater representation of minority classes in order to solve the dataset's intrinsic imbalance, and feature scaling enhances model convergence. With an accuracy of 98.8%, Random Forest was the most accurate model for this challenge. The outcomes demonstrate that the system is able to precisely forecast traffic in real-time, which aids in route planning, traffic control, and enhancing urban mobility.

Keywords

Machine Learning, GTFS, Random Forest, Cross-Validation, Real-Time Prediction, Congestion Detection


Citation of this Article

Ali Atta Gheni. (2024). Urban Traffic Congestion Prediction Using GTFS Data and Advanced Machine Learning Models. International Research Journal of Innovations in Engineering and Technology - IRJIET, 8(10), 25-31. Article DOI https://doi.org/10.47001/IRJIET/2024.810005

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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