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.


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    Titel :

    Improved Road Traffic Congestion Prediction Using Machine Learning through Modified Index


    Weitere Titelangaben:

    Advances in intell. Systems Research


    Beteiligte:
    Bhalerao, Shilpa (Herausgeber:in) / Gupta, Praveen (Herausgeber:in) / Kate, Vandana (Herausgeber:in) / Soni, Deepti (Autor:in) / Masih, Shraddha (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    25.05.2025


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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