Data-driven risk analysis of nonlinear factor interactions in road safety using Bayesian networks.

ABSTRACT: This paper aims to demonstrate nonlinear risk factor interactions based on a data-driven approach using a Bayesian network model, providing a road safety use case. Road safety is a critical issue worldwide, with approximately 1.3 million road traffic deaths each year (WHO). Traditional road safety risk assessment methods often analyze individual factors separately; however, these assessments fail to capture the complex dynamics of real-world analysis, in which multiple factors interact through nonlinear relationships. In this study, a novel road safety risk assessment approach that uses a Bayesian network model to explore the nonlinear relationships among road safety risk factors is developed. Through the analysis of extensive crash reports from the state of Maryland, the complex interdepe

SUBMITTER: Carrodano C 

PROVIDER: S-EPMC11327359 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature