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From Prediction to Prevention: Using Text Mining and Explainable Machine Learning for Urban Bus Accident Analytics.

Bowei Chen¹, Yufei Huang², Yu Zheng³

¹Adam Smith Business School, University of Glasgow, Glasgow, UK.

Risk Analysis : an Official Publication of the Society for Risk Analysis

|January 30, 2026

Summary

This summary is machine-generated.

This study uses machine learning to analyze urban bus accidents, identifying key risk factors like scooter collisions and sudden stops. The findings offer actionable insights for improving transportation safety.

Keywords:

SHapley Additive exPlanations bus accident analytics explainable machine learning topic modeling

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Area of Science:

Transportation Safety
Data Science
Risk Analysis

Background:

Urban bus accidents pose significant safety and operational challenges in densely populated areas.
Accident data often contains unstructured narrative information crucial for understanding causes.
Existing analytical methods may lack the interpretability needed for effective safety interventions.

Purpose of the Study:

To develop and validate a machine learning framework for identifying, quantifying, and interpreting factors contributing to severe bus accidents.
To enhance the predictive accuracy and interpretability of accident severity models by integrating unstructured text data.
To provide actionable insights for stakeholders to implement targeted safety measures.

Main Methods:

Integration of a structural topic model (STM) for extracting accident scenarios from narrative data.
Utilization of an extreme gradient boosting (XGBoost) classifier for predicting accident severity.
Application of SHapley Additive exPlanations (SHAP) for global and local interpretation of model predictions.

Main Results:

The machine learning framework significantly improved predictive accuracy and interpretability by incorporating text-derived accident patterns.
Key risk factors identified include rear-end collisions with electric scooters, sudden stops causing passenger injuries, and left-turn maneuvers in traffic congestion.
SHAP analysis provided clear, actionable insights into accident causation at both overall and specific incident levels.

Conclusions:

The developed analytical framework effectively integrates structured and unstructured data for interpretable risk modeling in transportation.
Findings offer practical guidance for drivers, transit operators, and policymakers to mitigate urban bus accident risks.
The modular approach provides a transferable foundation for risk analysis in various transportation and safety domains.