Aiming at the problems of complicated traffic flow and high accident safety risks in the expressway merging area, considering the discrete and heterogeneous characteristics of traffic conflict data, a Poisson-lognormal distribution model (PLN) and the random parameters Poisson-lognormal traffic conflict model (RP-PLN) were developed; The posterior distributions of the models parameters were estimated by Bayesian method and the Markov chain Monte Carlo (MCMC) simulation. The goodness-of-fit of models were compared by using the deviance information criterion. The results show that the goodness of fit of the random parameters Poisson-lognormal traffic conflict model (RP-PLN) is higher than that of the Poisson-lognormal distribution t model (PLN).


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    Title :

    Prediction of Traffic Conflict in Freeway Merging Area Based on Bayesian Model


    Contributors:
    Lian, Meng (author) / Liu, Borong (author) / Luo, Jing (author)


    Publication date :

    2021-11-12


    Size :

    523938 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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