Road accident data analysis using Bayesian networks

Abstract

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.

Keywords:

• Bayesian networks
• Directed acyclic graph
• Maximum-likelihood estimation
• PC algorithm
• Road accident data
• Structure learning

Acknowledgements

The authors are grateful to Research Center of Tehran Traffic Police for providing the accident data. The authors are also grateful to the editor and anonymous reviewers for several helpful suggestions which significantly improved the quality of this paper.