Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models
Abstract
This study presents a deep tabular learning framework for predicting crash severity in electric vehicle (EV) collisions using real-world crash data from Texas (2017-2023). After filtering for electric-only vehicles, 23,301 EV-involved crash records were analyzed. Feature importance techniques using XGBoost and Random Forest identified intersection relation, first harmful event, person age, crash speed limit, and day of week as the top predictors, along with advanced safety features like automatic emergency braking. To address class imbalance, Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTEENN) resampling was applied. Three state-of-the-art deep tabular models, TabPFN, MambaNet, and MambaAttention, were benchmarked for severity prediction. While TabPFN demonstrated strong generalization, MambaAttention achieved superior performance in classifying severe injury cases due to its attention-based feature reweighting. The findings highlight the potential of deep tabular architectures for improving crash severity prediction and enabling data-driven safety interventions in EV crash contexts.
Cite
@article{arxiv.2509.11449,
title = {Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models},
author = {Shriyank Somvanshi and Pavan Hebli and Gaurab Chhetri and Subasish Das},
journal= {arXiv preprint arXiv:2509.11449},
year = {2026}
}
Comments
This is the author's preprint version of a paper accepted for presentation at the 24th International Conference on Machine Learning and Applications (ICMLA 2025), December 3-5, 2025, Florida, USA. The final published version will appear in the official IEEE proceedings. Conference site: https://www.icmla-conference.org/icmla25/