Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists

Young motorcyclists, particularly those aged 15–24, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet non-use. This study aims to identify key factors influencing crash severity by analyzing 10,726 young motorcyclist crashes in Texas from 2017 to 2022. Two advanced tabular deep learning models, ARM-Net and MambaNet, were employed, using an advanced resampling technique to address class imbalance. The models were trained to classify crashes into three severity levels: Fatal/Severe (KA), Moderate/Minor (BC), and No Injury (O). ARM-Net achieved an accuracy of 87%, outperforming MambaNet's 86%, with both models excelling in predicting severe and no-injury crashes while facing challenges in moderate crash classification. Key findings highlight the significant influence of demographic, environmental, and behavioral factors on crash outcomes. The study underscores the need for targeted interventions, including stricter helmet enforcement and educational programs customized to young motorcyclists. These insights provide valuable guidance for policymakers in developing evidence-based strategies to enhance motorcyclist safety and reduce crash severity.