Evaluating crash risk factors of farm equipment vehicles on county and non-county roads using interpretable tabular deep learning (TabNet)

Abstract

Crashes involving farm equipment vehicles are a significant safety concern on public roads, particularly in rural and agricultural regions. These vehicles display unique challenges due to their slow-moving operational speed and interactions with faster vehicles, often leading to severe crashes. This study analyzed crashes involving farm equipment vehicles to examine the factors influencing crash severity, with a particular focus on comparing incidents on county roads to those on non-county roads. The dataset included key variables such as road geometry, lighting conditions, and traffic interactions, with preprocessing techniques like Synthetic Minority Over-sampling Technique (SMOTE) applied to address class imbalance. The TabNet model, a tabular deep learning model, was employed to analyze crash dynamics, offering both predictive accuracy and interpretability through feature importance and SHapley Additive exPlanations (SHAP) plots.

Findings revealed that crash severity on county roads is primarily influenced by crash speed limit, first harmful event, traffic control, and person age, reflecting the role of road geometry and demographic risk in rural settings. In contrast, non-county roads were more affected by lighting conditions, intersection-related features, and population group, emphasizing the impact of visibility and traffic complexity in urban areas. Speed limit consistently emerged as a critical factor across all road types and severity levels. The study emphasized the need for targeted safety interventions, including visibility enhancements, speed management, and enhanced education campaigns for county and non-county areas.