The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and development of urban transport systems. Monitoring and accurately forecasting urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion. Compared with traditional short-time traffic prediction, this study proposes a machine learning algorithm-based traffic forecasting model for daily-level peak hour traffic operation status prediction by using abundant historical data of urban traffic performance index (TPI). The study also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation, including day of week, time period, public holiday, car usage restriction policy, special events, etc. Based on long-term historical TPI data, this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm (XGBoost). The model validation results show that the model prediction accuracy can reach higher than 90%. Compared with other prediction models, including Bayesian Ridge, Linear Regression, ElatsicNet, SVR, the XGBoost model has a better performance, and proves its superiority in large high-dimensional data sets. The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.


    Access

    Download


    Export, share and cite



    Title :

    Extreme gradient boosting algorithm based urban daily traffic index prediction model: a case study of Beijing, China


    Contributors:
    Jiancheng Weng (author) / Kai Feng (author) / Yu Fu (author) / Jingjing Wang (author) / Lizeng Mao (author)


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown





    GIS-based analysis of urban traffic model: Case study in Beijing

    Liang Yongkai / Li Jingyuan / Liu Hai | DOAJ | 2021

    Free access


    Predicting the Future Signalization of Traffic-Actuated Signals Using Extreme Gradient Boosting

    Heckmann, Kevin / Budde, Jannik / Schneegans, Lena Elisa et al. | Transportation Research Record | 2024


    Extreme Gradient Boosting Machine Learning Algorithm For Safe Auto Insurance Operations

    Dhieb, Najmeddine / Ghazzai, Hakim / Besbes, Hichem et al. | IEEE | 2019