Accurate traffic congestion forecasting is an indispensable element of urban transport systems. This paper suggests a machine learning model to predict rush-hour traffic congestion using a newly defined Traffic Congestion Index (M_TCI), incorporating traffic density as a crucial factor for congestion prediction. This study uses XGBoost algorithm with spatio-temporal and contextual features such as holidays and seasonality to enhance the model’s accuracy. The model focuses on long-term prediction, incorporating the day of the week, time, holiday and seasonality to predict daily road network performance. Results show that the model outperforms ensemble models- CatBoost, Gradient Boosting Machine (GBM) and LightGBM and achieves an accuracy of 90%. XGBoost performs better in handling large and high-dimensional datasets, making it a valuable tool for predicting traffic congestion and optimizing urban road networks.


    Access

    Download


    Export, share and cite



    Title :

    Improved Road Traffic Congestion Prediction Using Machine Learning through Modified Index


    Additional title:

    Advances in intell. Systems Research


    Contributors:

    Conference:

    International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications ; 2025 ; Indore, India February 07, 2025 - February 08, 2025



    Publication date :

    2025-05-25


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    Traffic Congestion Index Estimation for Road Using Fuzzy Logic

    Reddy, Bommireddy Vijay Kumar / Khan, Zoheib Tufail / Reddy, S.KeerthiNandan et al. | IEEE | 2022


    Machine learning Smart Traffic Prediction and Congestion Reduction

    Lakshna, A. / Ramesh, K. / Prabha, B. et al. | IEEE | 2021


    Traffic congestion prediction using machine learning: Amman City case study

    Arabiat, Areen / Hassan, Mohammad / Momani, Omar Al | SPIE | 2024



    Road traffic prediction and congestion control using Artificial Neural Networks

    More, Rohan / Mugal, Abhishek / Rajgure, Sheetal et al. | IEEE | 2016