# Exploring Factors Affecting Pedestrian Crash Severity Using TabNet: A Deep Learning Approach

arXiv: https://arxiv.org/abs/2312.00066 · HTML: https://mlsanity.com/paper/2312.00066/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 103 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities](https://mlsanity.com/paper/2008.08221/) (2020)
- [Assessing Omitted Variable Bias when the Controls are Endogenous](https://mlsanity.com/paper/2206.02303/) (2022)

## Built on by (most cited first, 4 of 4)

- [Applying Tabular Deep Learning Models to Estimate Crash Injury Types of Young Motorcyclists](https://mlsanity.com/paper/2503.10474/) (2025)
- [Crash Severity Analysis of Child Bicyclists using Arm-Net and MambaNet](https://mlsanity.com/paper/2503.11003/) (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)
- [Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach](https://mlsanity.com/paper/2406.06624/) (2024)

## Abstract (arXiv)

This study presents the first investigation of pedestrian crash severity using the TabNet model, a novel tabular deep learning method exceptionally suited for analyzing the tabular data inherent in transportation safety research. Through the application of TabNet to a comprehensive dataset from Utah covering the years 2010 to 2022, we uncover intricate factors contributing to pedestrian crash severity. The TabNet model, capitalizing on its compatibility with structured data, demonstrates remarkable predictive accuracy, eclipsing that of traditional models. It identifies critical variables, such as pedestrian age, involvement in left or right turns, lighting conditions, and alcohol consumption, which significantly influence crash outcomes. The utilization of SHapley Additive exPlanations (SHAP) enhances our ability to interpret the TabNet model's predictions, ensuring transparency and understandability in our deep learning approach. The insights derived from our analysis provide a valuable compass for transportation safety engineers and policymakers, enabling the identification of pivotal factors that affect pedestrian crash severity. Such knowledge is instrumental in formulating precise, data-driven interventions aimed at bolstering pedestrian safety across diverse urban and rural settings.