Analyzing the Transportation Sector’s Safety Incidents: Insights from a Hierarchical Multi-Factor Regression Model

The transportation sector’s construction workforce faces significant safety risks, with a high rate of injuries influenced by a complex interplay of factors. This study presents a novel methodology to analyze and interpret key factors contributing to safety incidents, using a structured multiple linear regression (MLR) approach. By integrating top-level data from the Bureau of Labor Statistics, the study examines both internal (e.g., demographic characteristics) and external (e.g., time of day, event type) factors that impact safety. Five high-performing MLR models identify critical combinations of variables linked to incidents, revealing targeted risk profiles within the workforce. This framework addresses data limitations often faced in safety analysis and offers actionable insights for improving safety protocols and reducing injury rates in transportation construction. The findings would empower state Departments of Transportation and industry stakeholders to implement strategic interventions aimed at mitigating specific safety risks and promoting a safer work environment.