Road Traffic Accident Severity Prediction: An Intelligent Analysis Tool Based on Machine Learning

AlphaVizi's open-source project 'Road Traffic Accident Severity Prediction' is an intelligent analysis tool based on machine learning. It predicts accident casualty severity by analyzing road traffic data, identifies key influencing factors, and provides a zero-code desktop application to support traffic safety management, accident prevention, and related fields.

Road traffic accidents are a major global public safety issue. According to WHO data, about 1.35 million people die from road traffic accidents each year, and tens of millions are injured. Effectively predicting accident severity and identifying high-risk factors are of great significance for accident prevention and emergency response, which is the demand this project addresses.

Data Preprocessing Module

Automatically performs data cleaning, format standardization, feature encoding, and data splitting to ensure input data quality.

Exploratory Data Analysis (EDA)

Provides visualization functions such as statistical charts, trend analysis, geographic distribution, and multi-dimensional analysis to help understand data patterns.

Correlation Analysis

Identifies key factors related to accident severity through Pearson correlation coefficient, Spearman rank correlation, chi-square test, and heatmaps.

Classification Prediction Models

Supports algorithms like decision trees, random forests, SVM, logistic regression, and gradient boosting trees, and automatically completes training, validation, and evaluation.

Decision Tree Visualization and Interpretation

Provides tree structure display, path tracking, feature importance quantification, and rule extraction to enhance model interpretability.

Traffic Management Departments

Used for risk assessment (identifying high-risk road sections/time periods), accident prevention, resource allocation, and policy formulation.

Insurance Companies

Used for claim prediction, risk assessment (adjusting premiums), and fraud detection.

Academic Research Institutions

Used for hypothesis testing, feature engineering, model comparison, and paper publication.

System Requirements

• OS: Windows 10+/macOS 10.14+/Linux
• Memory: At least 4GB RAM
• Disk: At least 500MB of space
• Network: Required for downloading the application

Installation and Startup

1. Download the installation package for your system from GitHub Releases
2. Run the installer to complete installation
3. Launch the application

Analysis Process

1. Load CSV format data
2. Click Analyze to generate a visualization report
3. Select fields to predict severity
4. View the results panel

• Zero-code experience: Graphical interface, easy to use for non-technical users.
• End-to-end automation: Fully automatic from data loading to result output (cleaning, feature engineering, model training, visualization).
• Interpretability first: Features like decision tree visualization and feature importance help users understand the model's decision logic.
• Cross-platform support: Compatible with Windows, macOS, and Linux systems.

Current Limitations

1. Data format limitations: Mainly supports CSV; limited support for databases/APIs.
2. Algorithm selection: Advanced users cannot customize parameters.
3. Real-time prediction: Focuses on batch analysis; weak real-time stream processing capability.
4. Model deployment: Lacks export and integration functions.

Improvement Directions

1. Expand data sources: Support databases, data warehouses, and real-time streams.
2. AutoML: Automatic feature engineering and hyperparameter optimization.
3. Model serviceization: Provide REST API for online prediction.
4. Deep learning: Explore neural network applications.
5. GIS integration: Combine map data for spatial analysis.

This project demonstrates the application potential of machine learning in the public safety field, lowering the threshold for AI use through an easy-to-use desktop application. Its interpretability design (e.g., decision tree visualization) is particularly important in personal safety scenarios. With the development of intelligent transportation in the future, we expect the project to integrate more data sources and algorithms to make greater contributions to road traffic safety, and hope more similar tools emerge to let AI serve social well-being.