Feature group tabular transformer: a novel approach to traffic crash modeling and causality analysis

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Abstract

Reliable and interpretable traffic crash modeling is essential for understanding causality and improving road safety. This study introduced a novel approach to predicting crash types by utilizing a comprehensive dataset fused from multiple sources, including weather data, crash reports, high-resolution traffic information, pavement geometry, and facility characteristics. An essential part of our proposed approach was a feature group tabular transformer (FGTT) model, which organizes disparate data into meaningful feature groups, represented as tokens. These group-based tokens serve as rich semantic components, enabling effective identification of collision patterns and interpretation of causal mechanisms. The FGTT model was compared with widely used tree ensemble models, including random forest, XGBoost, and CatBoost, demonstrating better predictive performance. Furthermore, the attention heatmaps from the FGTT model revealed key influential factor interactions, providing fresh insights into the underlying causality of distinct crash types.

Keywords

road safety / crash analysis and modeling / causality analysis / tabular transformer / tree ensemble / feature grouping / attention heatmap

CHANGE HISTORY

A Correction to this paper has been published:
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This article has been retracted.Please see the Retraction Notice for more detail:
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