Road Traffic Accident Severity Prediction: An Intelligent Early Warning System Based on Machine Learning

This project uses machine learning technology to predict the severity of traffic accidents based on multi-dimensional factors such as driver information, vehicle characteristics, road conditions, and weather conditions, and implements real-time prediction through an interactive web application built with Streamlit.

• Original Author/Maintainer: adhithya20002
• Source Platform: GitHub
• Original Title: road-accident-severity-prediction
• Original Link: https://github.com/adhithya20002/road-accident-severity-prediction
• Publication Date: June 3, 2026

---

Road traffic accidents are a global public safety issue. According to the World Health Organization, approximately 1.35 million people die from road traffic accidents each year, and tens of millions more are injured or disabled. In addition to casualties, traffic accidents also cause huge economic losses and social burdens.

In this context, technology that can predict accident severity has important social value:

• Emergency Response Optimization: When a severe accident is predicted, more rescue resources can be automatically dispatched
• Preventive Measure Formulation: Identify high-risk scenarios and take preventive measures in advance
• Insurance Pricing: Help insurance companies assess risks more accurately
• Urban Planning: Identify accident-prone areas and guide road improvements

This project is based on this demand, using machine learning technology to build an accident severity prediction model, and through a web application format, making it easy for non-technical users to use.

---

The project follows a standard machine learning engineering process, forming a complete closed loop from data collection to model deployment:

The project uses the RTA (Road Traffic Accident) dataset, which contains multi-dimensional information related to road traffic accidents. Such data usually comes from the accident reporting systems of traffic management departments.

Raw data often has problems such as missing values, outliers, and inconsistent formats. The preprocessing stage includes:

• Handling missing data
• Outlier detection and handling
• Data type conversion
• Duplicate record handling

Understand data distribution and feature relationships through visual analysis:

• Category distribution of accident severity
• Correlation between each feature and severity
• Correlation analysis between features
• Temporal and geographical distribution patterns

Convert raw data into features usable by the model:

• Categorical variable encoding (Label Encoding / One-Hot Encoding)
• Numerical feature standardization
• Feature selection (removing redundant or irrelevant features)
• May also include feature combination or derived feature creation