Prediction of road traffic from multiple sources using Gaussian approach is most import in intelligent transport systems. Existing works are only focused on non-intrusive sensors that are very expensive. Sensors are detecting traffic conditions and image recognition, etc. The maintenance of these sensors is very difficult, and to address the issue, this paper aims to improve road traffic speed prediction by using tweet sensors and social media. This includes many challenges, including location uncertainty of low-resolution data, language ambiguity of traffic description in text, etc. To response these challenges, we provide a uniform modeling probabilistic framework called Topic Enhanced Gaussian Aggregation model (TEGPAM). It consists of three components location disaggregation model, traffic topic model, and traffic speed Gaussian model. The module is designed with the features of a typical social web base, with functions that are related to the proposed model, and different driving directions are referred to as different road links.


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    Titel :

    Identification of Road Traffic from Multiple Sources Using Modern Gaussian Approach


    Weitere Titelangaben:

    CogScienceTechnology


    Beteiligte:
    Kumar, Amit (Herausgeber:in) / Ghinea, Gheorghita (Herausgeber:in) / Merugu, Suresh (Herausgeber:in) / Hashimoto, Takako (Herausgeber:in) / Jegadeesan, R. (Autor:in) / Choudhary, Ayush (Autor:in) / Pundir, Abhinav (Autor:in) / Rehaan, Baha Ur (Autor:in)


    Erscheinungsdatum :

    01.11.2022


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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