DEVELOPMENT OF A CAUSAL RELATIONSHIP MODEL OF BEHAVIORAL RISK FACTORS IN DANGEROUS GOODS TRANSPORTATION USING ADAS AND DMS DATA
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Abstract
This study aimed to 1) examine the relationship between Driver State Warning (DSW) and Headway Monitoring Warning (HMW), 2) investigate the relationship between Headway Monitoring Warning (HMW) and Forward Collision Warning (FCW), 3) examine the relationship between Forward Collision Warning (FCW) and Pedestrian Collision Warning (PCW), and 4) develop a causal relationship model of behavioral risk factors in dangerous goods transportation using data obtained from a Driver Monitoring System (DMS) and an Advanced Driver Assistance System (ADAS). The study was conducted through a field experiment involving 10 heavy-duty vehicles operating under real-world conditions, with each vehicle monitored over a distance of 1,000 kilometers. The variables investigated included Driver State Warning (DSW), Headway Monitoring Warning (HMW), Forward Collision Warning (FCW), and Pedestrian Collision Warning (PCW), which were structured within a conceptual framework describing the progression of risk from driver conditions to higher levels of collision risk. Data were analyzed using descriptive statistics, Spearman’s rank correlation, and sequential linear regression to evaluate the relationships among variables and develop the proposed causal model.
The results indicated that all variables exhibited non-normal distributions characterized by high levels of skewness and kurtosis, reflecting the irregular occurrence of risk-related events under real-world driving conditions. Correlation analysis revealed significant positive relationships between HMW and FCW (ρ = 0.656, p = 0.039) and between FCW and PCW (ρ = 0.636, p = 0.048). Regression analysis demonstrated that DSW had a significant positive effect on HMW (β = 0.967, p < 0.001, R² = 0.935), HMW had a significant positive effect on FCW (β = 0.971, p < 0.001, R² = 0.942), and FCW had a significant positive effect on PCW (β = 0.972, p < 0.001, R² = 0.946). These findings support the proposed causal relationship model, indicating that risk propagates from driver conditions to unsafe driving behaviors and subsequently to higher levels of collision risk. The study highlights the potential of DMS and ADAS data in explaining behavioral risk mechanisms and provides a foundation for the development of safety models, proactive risk assessment frameworks, and accident prevention strategies in dangerous goods transportation.
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