# Predicting Traffic Accident Severity with Deep Neural Networks

arXiv: https://arxiv.org/abs/2509.03819 · HTML: https://mlsanity.com/paper/2509.03819/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights](https://mlsanity.com/paper/1909.09638/) (2019)
- [A Countrywide Traffic Accident Dataset](https://mlsanity.com/paper/1906.05409/) (2019)

## Abstract (arXiv)

Traffic accidents can be studied to mitigate the risk of further events. Recent advances in machine learning have provided an alternative way to study data associated with traffic accidents. New models achieve good generalization and high predictive power over imbalanced data. In this research, we study neural network-based models on data related to traffic accidents. We begin analyzing relative feature colinearity and unsupervised dimensionality reduction through autoencoders, followed by a dense network. The features are related to traffic accident data and the target is to classify accident severity. Our experiments show cross-validated results of up to 92% accuracy when classifying accident severity using the proposed deep neural network.