This paper recommends the rolling optimization strategy based on the initial data of road traffic accidents, and builds the rolling optimization-grey Markov dynamic prediction model, which can effectively resolve the matter that the precision of accident forecast is influenced by the time benefit of the predicted data. In order to predict the development tendency of road traffic accidents and further improve the prediction precision of random time series, this paper uses Markov chain theory to probe into the transition law between different states. The case study shows that this measure has good forecast precision and practicability in a certain period of time, and can offer Reference for road traffic accident forecast and traffic safety warning.


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    Title :

    Forecast of Road Traffic Accidents grounded on Rolling optimization Grey Markov Model


    Contributors:
    Xu Chengzhen (author) / Zhang Heng (author) / Gong Yanan (author) / Sang Huiyun (author) / Sun Guanglin (author) / Chen Jing (author)


    Publication date :

    2020




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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