Traffic Accident Data Mining Analysis: A Complete Practice from Data Cleaning to Insight Discovery

This project is a complete traffic accident data mining practice published by Mohammad Rasoulian on GitHub. Using Python ecosystem tools (Pandas, Seaborn, Plotly, etc.), it performs preprocessing, feature engineering, statistical analysis, and visualization on accident data, exploring the correlation patterns between driver information, road conditions, weather factors, and accident causes, aiming to extract actionable insights that can be used to improve road safety.

Background: Traffic accidents are influenced by multiple factors such as driver age, experience, road surface conditions, and weather. Understanding these relationships is crucial for accident prevention. Dataset: Uses RTA Dataset.csv, which includes key content like time information, driver information (gender, age group, driving experience), vehicle information, location information, road conditions, environmental factors, and accident characteristics (collision type, cause, severity).

Data processing is divided into three stages:

1. Exploration: Use Pandas info()/describe() to analyze structure, pairplot to visualize variable relationships, and identify missing values/outliers;
2. Cleaning: Standardize column names, lowercase text, handle missing values, convert time to datetime, and replace invalid values;
3. Feature Engineering: Convert age/experience ranges to numerical values, gender encoding, and one-hot encoding for categorical features.

Analysis Content:

• Accident cause distribution: Identify main triggers;
• Spatial density: Tree map shows regional accident density;
• Driver characteristics: Relationship between age/gender and accident rate;
• Road conditions: Correlation between driving experience and road surface type. Tools: Seaborn (statistical charts), Matplotlib (static plotting), Plotly (interactive visualization).

Findings: Clarify main accident causes, high-risk driver groups, and high-incidence areas. Applications:

• Traffic management: Optimize signals and patrols;
• Insurance pricing: Precise risk models;
• Policy formulation: Support regulation revisions;
• Vehicle design: Improve safety features;
• Urban planning: Guide road infrastructure.

Improvement Directions:

1. Predictive modeling: Accident severity classification model;
2. Pipeline optimization: Modular preprocessing, complex missing value imputation;
3. Deployment: Interactive dashboard/Web application. Gains: Skills in real data cleaning, categorical/missing data handling, EDA, visualization, and feature engineering.