This project aims to create an innovative tollgate traffic prediction system using cutting-edge machine learning (ML) algorithms, including RF (Random Forest), DT (Decision Trees), and boosting techniques. Contrasting traditional methods, it covers all types of vehicles, like cars, buses, and 4-wheelers, giving us a complete picture of traffic dynamics. We start by gathering and preprocessing data carefully, making sure to include various vehicle types that you might see in the real world. We then turn this raw data into meaningful predictors, setting the stage for our robust predictive models. We use different algorithms like GBR (Gradient Boosting Regressor), Lasso, and Ridge to find patterns in the data, and a comparative analysis helps us understand how well each one works for different types of vehicles. We find that ensemble methods are particularly effective for tollgate traffic prediction, showing us the strengths and weaknesses of each algorithm. Besides just predicting traffic, this study can help with transportation planning and developing better infrastructure by giving us insights into what types of vehicles use tollgates. This research advances our understanding of tollgate traffic prediction systems and sets the stage for future innovations in smart transportation. This can help policymakers, urban planners, and researchers navigate the challenges of modern transportation. Overall, this project improves our ability to predict tollgate traffic and contributes to making our roads safer and more efficient for everyone.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Integrated Traffic Forecasting System Using Machine Learning


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

    International Conference on Soft Computing and Signal Processing ; 2024 ; Hyderabad, India June 20, 2024 - June 21, 2024



    Publication date :

    2025-05-25


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    A Traffic Congestion Forecasting Model using CMTF and Machine Learning

    Chowdhury, Md. Mohiuddin / Hasan, Mahmudul / Safait, Saimoom et al. | IEEE | 2018


    Machine Learning Methods for Predicting Traffic Congestion Forecasting

    Rele, Mayur / Julian, Anitha / Patil, Dipti et al. | Springer Verlag | 2024


    TRAFFIC FORECASTING SYSTEM, TRAFFIC FORECASTING DEVICE, AND TRAFFIC FORECASTING METHOD

    IWAE TOMOHIDE / OBA YOSHIKAZU / NARUSE KOSUKE et al. | European Patent Office | 2021

    Free access

    Traffic Flow Forecasting in Intelligent Transportation Systems Prediction Using Machine Learning

    Hossain, Mohammad Naveed / Ahmed, Nafim / Wazid Ullah, S. M. | IEEE | 2022


    A Hybrid Machine Learning Approach for Accurate Railway Traffic Forecasting

    Vinayagam, P / S, Kanagamalliga / Hariharan, M.V. et al. | IEEE | 2024