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

TL;DR

This paper presents an interpretable clustering method for analyzing safety climate perceptions among truck drivers, comparing multiple algorithms and introducing new interpretability measures to better understand driver group differences.

Contribution

It introduces a novel, interpretable clustering approach for safety climate analysis, compares multiple algorithms, and proposes a new method for evaluating partial dependence plots.

Findings

Supervisory care promotion significantly distinguishes driver groups.

The proposed method improves interpretability of clustering results.

Analysis based on data from over 7,000 truck drivers.

Abstract

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.