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The transportation industry, particularly the trucking sector, is prone toworkplace accidents and fatalities. Accidents involving large trucks accountedfor a considerable percentage of overall traffic fatalities. Recognizing thecrucial role of safety climate in accident prevention, researchers have soughtto understand its factors and measure its impact within organizations. Whileexisting data-driven safety climate studies have made remarkable progress,clustering employees based on their safety climate perception is innovative andhas not been extensively utilized in research. Identifying clusters of driversbased on their safety climate perception allows the organization to profile itsworkforce and devise more impactful interventions. The lack of utilizing theclustering approach could be due to difficulties interpreting or explaining thefactors influencing employees' cluster membership. Moreover, existingsafety-related studies did not compare multiple clustering algorithms,resulting in potential bias. To address these issues, this study introduces aninterpretable clustering approach for safety climate analysis. This studycompares 5 algorithms for clustering truck drivers based on their safetyclimate perceptions. It proposes a novel method for quantitatively evaluatingpartial 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 truckdrivers, this study significantly contributes to the scientific literature. Ithighlights the critical role of supervisory care promotion in distinguishingvarious driver groups. The Python code is available athttps://github.com/NUS-DBE/truck-driver-safety-climate.

An interpretable clustering approach to safety climate analysis: examining driver group distinction in safety climate perceptions