# 🚦 Road Accident Risk Intelligence System
This project demonstrates a **Geospatial Risk Monitoring Dashboard** that detects when a driver enters accident-prone zones.
The system uses **PostgreSQL + PostGIS** for spatial analysis and **Streamlit** for interactive visualization.
## 📌 Project Objective
To build a system that:
• Stores accident hotspot data
• Calculates accident severity levels
• Generates spatial buffer zones around dangerous areas
• Tracks a driver's route
• Detects when the driver enters risk zones
• Displays real-time alerts on a map dashboard
## 🗺 System Architecture
PostgreSQL + PostGIS
↓
Spatial Risk Analysis
↓
Streamlit Dashboard
↓
OpenStreetMap Visualization
↓
Real-time Driver Alerts
## ⚙️ Technologies Used
• PostgreSQL
• PostGIS
• Streamlit
• Python
• Folium
• GeoPandas
• Shapely
• OpenStreetMap
## 📊 Key Features
### Accident Risk Analysis
- Calculates accident **severity index**
- Classifies areas into **High / Medium / Low risk**
### Buffer-Based Risk Zones
- Generates **200m spatial buffer zones**
- Highlights dangerous locations
### Driver Path Simulation
- Uses a real **LineString route**
- Simulates vehicle movement along the path
### Intelligent Alert System
The dashboard shows alerts when the driver:
🚨 Enters an accident-prone area
✅ Leaves the danger zone
## 🗂 Database Structure
Main Tables:
• accident_data1
• driver_path
Views:
• high_risk_accidents
• driver_alerts
• driver_risk_summary
##
Install dependencies:
pip install -r requirements.txt
Run the application:
streamlit run app.py
The dashboard will open in your browser.
## 🧠 Example Dashboard Workflow
1️⃣ Load accident hotspots
2️⃣ Display risk buffers
3️⃣ Show driver route
4️⃣ Simulate driver movement
5️⃣ Trigger alerts when entering danger zones
## 📍 Future Improvements
• Real-time GPS driver tracking
• Traffic data integration
• Accident heatmaps
• Machine learning risk prediction
• Mobile alert notifications
## 👨💻 Author
Geospatial Risk Intelligence Project