Bayesian Networks (BNs) are graphical probabilistic models representing the joint probability function over a set of random variables using a directed acyclic graphical structure. In this paper, we consider a road accident data set collected at one of the popular highways in Iran. Implementing the well-known parents and children algorithm, as a constraint-based approach, we construct a BN model for the available accident data. Once the structure of the BN is learnt, we concentrate on the parameter-learning task. We compute the maximum-likelihood estimates of some parameters of interest, specifically, conditional probability of fatalities in the network.


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    Title :

    Road accident data analysis using Bayesian networks


    Contributors:

    Published in:

    Transportation Letters ; 9 , 1 ; 12-19


    Publication date :

    2017-01-01


    Size :

    8 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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