With the rapid economic growth, urban congestion has become an urgent problem to be solved in our country. Traffic overload prediction can well predict the flow of people and traffic and can effectively solve the problem of urban congestion. This paper analyzes the user’s orbit quality data and proposes a method to dynamically analyze the moving orbit to predict the position of the best orbit movement. Usually, people are familiar with the traffic situation in the fixed area where they live and can predict the regularity of traffic volume through preliminary forecast, so as to choose the best route to avoid congestion and improve efficiency. This paper mainly studies the effective solution to the problem of traffic congestion. By collecting the trajectory data generated by the traffic moving objects and studying the trajectory analysis method to predict the traffic congestion, it is helpful for people to effectively avoid the congestion. Use an improved pattern mining model to extract frequent patterns of trajectories and design a fast data structure DPT. The final results of the research show that when the minimum support is set to 5%, the non-dynamic execution time of PRED and the dynamic execution time of PRED are 0.86 s and 0.32%, respectively. On the basis of the established model, the dynamic prediction can be obtained faster. The next possible position of the moving object provides better technical conditions for traffic overload prediction and has practical significance in the application of intelligent traffic management.


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    Titel :

    Movement Trajectory Data Analysis Method Based on Traffic Overload Prediction


    Weitere Titelangaben:

    Lect. Notes on Data Eng. and Comms.Technol.


    Beteiligte:
    Jansen, Bernard J. (Herausgeber:in) / Zhou, Qingyuan (Herausgeber:in) / Ye, Jun (Herausgeber:in) / Shi, Liting (Autor:in)

    Kongress:

    International Conference on Cognitive based Information Processing and Applications ; 2022 ; Changzhou, China September 22, 2022 - September 23, 2022



    Erscheinungsdatum :

    2023-04-09


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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