  The transportation industry, particularly the trucking sector, is prone to
workplace accidents and fatalities. Accidents involving large trucks accounted
for a considerable percentage of overall traffic fatalities. Recognizing the
crucial role of safety climate in accident prevention, researchers have sought
to understand its factors and measure its impact within organizations. While
existing data-driven safety climate studies have made remarkable progress,
clustering employees based on their safety climate perception is innovative and
has not been extensively utilized in research. Identifying clusters of drivers
based on their safety climate perception allows the organization to profile its
workforce and devise more impactful interventions. The lack of utilizing the
clustering approach could be due to difficulties interpreting or explaining the
factors influencing employees' cluster membership. Moreover, existing
safety-related studies did not compare multiple clustering algorithms,
resulting in potential bias. To address these issues, this study introduces an
interpretable clustering approach for safety climate analysis. This study
compares 5 algorithms for clustering truck drivers based on their safety
climate perceptions. It proposes a novel method for quantitatively evaluating
partial dependence plots (QPDP). To better interpret the clustering results,
this study introduces different interpretable machine learning measures (SHAP,
PFI, and QPDP). Drawing on data collected from more than 7,000 American truck
drivers, this study significantly contributes to the scientific literature. It
highlights the critical role of supervisory care promotion in distinguishing
various driver groups. The Python code is available at
https://github.com/NUS-DBE/truck-driver-safety-climate.
