As an important part of modern transportation network, the flow management and congestion prevention of expressway have become a big challenge in urban planning and management. With the development of big data technology, using this technology to predict and manage traffic congestion on expressways has become the key to improve road use efficiency and driving safety. In the research process, firstly, a large number of collected data are cleaned and preprocessed, and then the data characteristics and potential influencing factors are explored by using data visualization method. Then, statistical analysis and machine learning technology are applied to construct multiple prediction models, and the effectiveness of each model is evaluated by cross-validation method, and finally the best model is selected for real-time congestion prediction. In addition, the data analysis under different road types and weather conditions is considered to enhance the generalization ability and accuracy of the model. In the morning and evening rush hours (such as 7–10 o'clock on Sect. 1 and 18–20 o'clock on Sect. 2), with the significant increase of traffic volume, the model accurately predicts the improvement of congestion level. The research results show that the developed prediction model can not only effectively predict the congestion on the expressway, but also provide decision support for traffic management departments, help them allocate traffic resources more reasonably, optimize traffic command, and reduce the economic losses and environmental impacts caused by traffic congestion.


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    Titel :

    Traffic Congestion Prediction Model for Smart Freeways Based on Big Data Analysis


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Patnaik, Srikanta (Herausgeber:in) / Tavana, Madjid (Herausgeber:in) / Jain, Vipul (Herausgeber:in) / Wen, Zhengxuan (Autor:in)

    Kongress:

    International Conference on Big Data Technology & Business Analytics ; 2024 ; Bhubaneswar, India July 18, 2024 - July 19, 2024



    Erscheinungsdatum :

    12.07.2025


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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