Due to massive increase in road vehicles, the rate of traffic accidents increases rapidly. These accidents cause injuries, disabilities and may even result in loss of lives. To analyze traffic accident data, one needs to understand and formalize the dataset. Mostly, this data is stored in relational databases or in raw text format. This paper proposes a framework to store traffic accident data in a big data platform (Casandra). This will enable fast and accurate analyses technique that use big data platforms. The paper proposes applying deep learning techniques to build prediction and classification models from the road accident data. In particular, the models will focus on the prediction and identification of hotspots on the roads in order to decrease the number of accidents in the future.


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    Titel :

    Towards Deep Learning based Traffic Accident Analysis


    Beteiligte:
    Naseer, Atif (Autor:in) / Nour, Mohamed K. (Autor:in) / Alkazemi, Basem Y. (Autor:in)


    Erscheinungsdatum :

    01.01.2020


    Format / Umfang :

    5890329 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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