Feature Engineering Framework for Traffic Accident Severity Prediction Based on Explainable AI

This project combines advanced machine learning techniques with Explainable AI (XAI) to build a feature engineering framework for traffic accident severity prediction. It aims to address the "black box" problem of traditional machine learning models and provide clear data insights and trustworthy basis for road safety decision-making.

Traffic accidents are a major global safety issue. According to the World Health Organization, about 1.35 million people die from road traffic accidents each year, and tens of millions are injured. Traditional traffic safety management relies on empirical judgment and post-event analysis, making it difficult to implement preventive interventions. With the development of big data and AI technologies, predicting accident severity using historical data has become possible. However, the decision-making process of "black box" models is hard to explain, which is a serious obstacle in high-risk fields—decision-makers need to understand the basis of model decisions to trust and adopt the recommendations.

Explainable AI (XAI) is a technical branch that makes the decision-making process of AI models understandable and trustworthy. It can reveal feature importance, provide local explanations, visualize decision boundaries, and detect potential biases. In traffic safety, the value of XAI includes: supporting policy formulation (identifying key risk factors), building public trust (transparent decision-making basis), and optimizing models (discovering data or model defects).

Feature engineering is a key link in machine learning projects and directly affects model performance. The feature engineering framework of this project includes four core components:

1. Data preprocessing layer: Handle missing values, outliers, and standardize data;
2. Feature extraction layer: Extract time (time period, season, etc.), space (road type, etc.), environment (weather, etc.), vehicle (type, etc.), and personnel (driving experience, etc.) features;
3. Feature selection layer: Filter relevant features and reduce dimensionality;
4. Model explanation layer: Integrate XAI tools such as SHAP and LIME to provide explanation support.

Common models for traffic accident severity prediction include: ensemble learning (random forest, XGBoost, etc.), deep learning (neural networks), and traditional statistical models (logistic regression, etc.). The XAI tools integrated in the project include: SHAP (quantify feature contributions), LIME (explain individual predictions), feature importance visualization, and partial dependence plots (show the relationship between features and predictions).

The project has significant application value:

• For traffic management departments: Realize risk early warning (identify high-risk road sections/time periods), optimize resources (allocate budgets reasonably), and evaluate policies (quantify the effect of measures);
• For the public: Push personalized safety tips and improve risk awareness;
• For the research field: Innovate methodologies (systematic application of XAI in traffic safety) and open-source sharing (provide reusable tool frameworks).

Current technical challenges include: data quality (incomplete records, inconsistent standards), class imbalance (few samples of severe accidents), causal inference (need to distinguish between correlation and causation), and model generalization (adaptability across regions/time periods). Future directions: real-time prediction (IoT + edge computing), multi-source data fusion (satellite, social media, etc.), causal discovery, and interactive visualization (easy to understand for non-technical personnel).

This project combines machine learning with XAI to provide a systematic feature engineering framework for traffic accident prediction. It not only pursues prediction accuracy but also focuses on the credibility and practicality of results, providing technical tools for intelligent traffic management and road safety improvement. With the accumulation of data and the progress of algorithms, such systems are expected to reduce accident casualties and losses and play a greater role.