There is active research being conducted into systems that can simulate a town's road traffic on a digital twin based on information sensed in the real world, making it possible to plan measures to alleviate congestion and optimize travel routes. A transportation digital twin is a computerized reproduction of real-world road traffic. By using a transportation digital twin, it is possible to grasp the state of road traffic in real time and predict future traffic conditions. In order to reproduce real-world traffic flows and predict future traffic flows using a digital twin, traffic data with fine temporal and spatial granularity is required. For this reason, research and development is being carried out based on 5G and Beyond 5G, which enable large-volume communication. However, this method requires the continuous collection of large amounts of data, which makes it difficult to utilize in regional cities in terms of cost. Therefore, we propose a method to build a digital twin by collecting small amounts of data, inputting them, and supplementing them with machine learning.


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    Title :

    Study on Digital Twin Computing for Predicting General Road Traffic Volume


    Contributors:


    Publication date :

    2025-02-18


    Size :

    1169953 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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