STARN-GAT: A Multi-Modal Spatio-Temporal Graph Attention Network for Accident Severity Prediction

TL;DR

STARN-GAT is a novel multi-modal spatio-temporal graph attention network that effectively models complex interdependencies among spatial, temporal, and contextual variables to predict accident severity with high accuracy and interpretability.

Contribution

It introduces a unified attention-based framework integrating road topology, traffic patterns, and environmental factors for accident severity prediction.

Findings

Achieves 85% Macro F1-score on FARS dataset

Attains 0.91 ROC-AUC and 81% recall for severe incidents

Validates effectiveness on South Asian traffic accident data

Abstract

Accurate prediction of traffic accident severity is critical for improving road safety, optimizing emergency response strategies, and informing the design of safer transportation infrastructure. However, existing approaches often struggle to effectively model the intricate interdependencies among spatial, temporal, and contextual variables that govern accident outcomes. In this study, we introduce STARN-GAT, a Multi-Modal Spatio-Temporal Graph Attention Network, which leverages adaptive graph construction and modality-aware attention mechanisms to capture these complex relationships. Unlike conventional methods, STARN-GAT integrates road network topology, temporal traffic patterns, and environmental context within a unified attention-based framework. The model is evaluated on the Fatality Analysis Reporting System (FARS) dataset, achieving a Macro F1-score of 85 percent, ROC-AUC of 0.91, and recall of 81 percent for severe incidents. To ensure generalizability within the South Asian context, STARN-GAT is further validated on the ARI-BUET traffic accident dataset, where it attains a Macro F1-score of 0.84, recall of 0.78, and ROC-AUC of 0.89. These results demonstrate the model's effectiveness in identifying high-risk cases and its potential for deployment in real-time, safety-critical traffic management systems. Furthermore, the attention-based architecture enhances interpretability, offering insights into contributing factors and supporting trust in AI-assisted decision-making. Overall, STARN-GAT bridges the gap between advanced graph neural network techniques and practical applications in road safety analytics.