<ul><li>This study uses tabular deep learning models, ARMNet and MambaNet, to analyze young motorcyclist crashes in Texas to identify key factors influencing crash severity.</li><li>ARMNet achieved an accuracy of 87 percent and outperformed MambaNet in predicting severe and no injury crashes.</li><li>The study highlights the significant influence of demographic, environmental, and behavioral factors on crash outcomes.</li><li>The findings emphasize the importance of targeted interventions and evidence-based strategies to enhance motorcyclist safety.</li></ul>

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

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