Traffic Accident Prediction

Project Overview

This project focuses on developing machine learning models to predict traffic accident severity and expected casualties. Using Random Forest algorithms, the system analyzes various traffic-related features to provide accurate predictions that can help in emergency response planning and traffic safety improvements.

The model emphasizes real-world interpretability, ensuring that predictions are not only accurate but also actionable for traffic management authorities and emergency services.

Key Features

Severity Classification

Multi-class prediction of accident severity levels based on environmental and traffic conditions

Casualty Prediction

Regression model to estimate expected number of casualties for resource allocation

Feature Analysis

Comprehensive analysis of contributing factors like weather, road conditions, and time of day

Real-time Processing

Optimized for quick predictions to support emergency response systems

Technical Implementation

Data Processing

Feature engineering from raw traffic data
Handling missing values and outliers
Categorical encoding for weather and road conditions
Temporal feature extraction (hour, day, season)

Model Architecture

Random Forest Classifier for severity prediction
Hyperparameter tuning using GridSearchCV
Feature importance analysis for interpretability
Cross-validation for robust performance metrics

Evaluation Metrics

Accuracy, Precision, Recall, F1-Score
Confusion matrix analysis
ROC-AUC curves for multi-class classification
Feature importance visualization

Results & Insights

87%

Classification Accuracy

0.92

ROC-AUC Score

15+

Key Features Identified

The model successfully identified weather conditions, time of day, and road type as the most significant factors in predicting accident severity, providing actionable insights for traffic safety planning.

Challenges & Solutions

Imbalanced Dataset

The dataset had significantly more minor accidents than severe ones. Addressed using SMOTE (Synthetic Minority Over-sampling Technique) and class weight adjustment.

Real-world Interpretability

Ensured model predictions are explainable using SHAP values and feature importance plots, making it practical for traffic authorities to use.

Missing Data

Developed intelligent imputation strategies based on temporal and spatial correlations in the traffic data.

Future Enhancements

Integration of real-time traffic camera feeds for dynamic prediction
Deployment as a web API for emergency services
Expansion to include geographic-specific models for different regions
Incorporation of social media data for real-time incident detection
Mobile application for on-the-go predictions