A probabilistic Bayesian inference model to investigate injury severity in automobile crashes

Abstract

Big data analytics examines millions, if not billions of records, to unmask hidden patterns, provide actionable insights and interpretable results for various domains. One area that has great potential to leverage the value of big data and analytics is the critical analysis of traffic accidents. Investigation results help in providing an in-depth understanding of the risks and provide measures to potentially prevent these risk factors hence enhancing the well-being of individuals who may experience such accidents. This study explains existing models and proposes a data science methodology in a field where probabilistic modeling makes much sense for faster, better decision-making. The main objective of this data analytics study is to identify the high-risk factors with their apparent significance to influence the probability of injury severity on automobile crashes using a geographically representative car crash dataset. To obtain reliable, accurate, and intuitive results, a multi-step probabilistic inference model based on Bayesian Belief Network— highly-acclaimed machine learning methodology—is proposed. The underlying inference model provides researchers with a causally accurate way to explore the domain (with the subject matter expert inputs) while disengaging issues related to statistical correlations and causal effects. In this study, we also used the data to create a web-based probabilistic inference simulator, a Bayesian inference decision support tool, which will be a publicly available/accessible tool, to help decision-makers better understand and to conduct what-if analysis on variable interdependencies.