Investigating heterogeneous human factor effects on crash severity: A counterfactual study in mountainous freeways

Accurately estimating the causal effect of human factors on crash severity in mountainous freeways is pivotal for developing effective safety strategies. Although previous studies have investigated human factors, they typically focus on estimating average effects under specific conditions, often conflating statistical correlations with causal relationships. Consequently, the underlying causal mechanisms in freeway crashes remain unclear. To address this limitation, this study proposes a dynamic weighted double machine learning framework that integrates LightGBM and XGBoost models to estimate confounder-outcome and treatment-outcome relationships. By optimizing model weights and using non-human-caused crashes as a control group, the effects of five human factors are isolated through counterfactual reasoning. The heterogeneous treatment effects of human factors on crash severity are quantified, and causal relationships are analyzed across variables such as weather, slope, truck traffic volume, and vehicle type. The results reveal significant heterogeneity in crash severity attributable to human factors compared to non-human factors. Inadequate safety distance often co-occurs with other high-risk conditions, amplifying crash severity. Reversing behavior is particularly sensitive to weather conditions. The causal pathways of distracted or fatigued driving and driving in the wrong lane are influenced by daily truck traffic volume and road slope. Additionally, interactions between slope and vehicle type significantly affect the severity of overloaded crashes. These findings underscore the need for targeted interventions addressing specific human factors in high-risk scenarios. Consequently, enforcement against reversing and overloading on steep slopes with high truck volume should be intensified, and heavy trucks should be restricted to right lanes on high-risk segments.