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

arXiv: https://arxiv.org/abs/2503.10474 · HTML: https://mlsanity.com/paper/2503.10474/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [ARM-Net: Adaptive Relation Modeling Network for Structured Data](https://mlsanity.com/paper/2107.01830/) (2021)
- [A Survey on Deep Tabular Learning](https://mlsanity.com/paper/2410.12034/) (2024)
- [MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs](https://mlsanity.com/paper/2210.17060/) (2022)
- [Exploring Factors Affecting Pedestrian Crash Severity Using TabNet: A Deep Learning Approach](https://mlsanity.com/paper/2312.00066/) (2023)
- [Feature Group Tabular Transformer: A Novel Approach to Traffic Crash Modeling and Causality Analysis](https://mlsanity.com/paper/2412.06825/) (2024)

## Built on by (most cited first, 2 of 2)

- [Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba](https://mlsanity.com/paper/2503.18970/) (2025)
- [Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models](https://mlsanity.com/paper/2509.11449/) (2025)

## Abstract (arXiv)

Young motorcyclists, particularly those aged 15 to 24 years old, face a heightened risk of severe crashes due to factors such as speeding, traffic violations, and helmet usage. 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, ARMNet 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 or Severe, Moderate or Minor, and No Injury. ARMNet achieved an accuracy of 87 percent, outperforming 86 percent of Mambanet, 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.