Intelligent Road Accident Risk Prediction Platform
RoadVision AI is a next-generation, mobile app & web-based platform designed to predict road accident risk before a journey begins. By combining artificial intelligence, public road accident data, weather intelligence, and geospatial analysis, the system identifies high-risk road segments and travel conditions and presents them through an interactive location-based interface.
Unlike traditional reactive road safety systems, RoadVision AI focuses on prediction and prevention, enabling data-driven awareness and safer route decision-making. The platform is fully software-based, requiring no physical sensors or hardware, making it scalable, cost-effective, and easy to deploy.
Road accidents often occur due to a combination of factors such as:
- Poor weather conditions
- Time-dependent risk patterns (night, peak hours)
- Accident-prone road segments
- Lack of risk awareness before travel
Most existing systems respond after accidents occur.
RoadVision AI addresses this gap by predicting accident risk in advance, using historical and contextual data.
RoadVision AI analyzes multiple data dimensions to estimate accident risk:
- Historical accident patterns
- Time-based features (hour, day, weekday/weekend)
- Weather conditions (rain, visibility, temperature)
- Geographical road location data
A machine learning model processes these inputs to generate:
- A risk score
- A risk category (Low / Medium / High)
- A visual risk representation on a location based interface
- 🔮 AI-Based Accident Risk Prediction
- 🗺️ Interactive Map Visualization
- 🌦️ Weather-Aware Risk Analysis
- ⏱️ Time-Sensitive Risk Modeling
- 🌐 Fully Web-Based (No Hardware Required)
- 🔐 Privacy-Friendly & Ethical Design
- 📈 Scalable for Smart Mobility Applications
- Python
- Pandas, NumPy
- Scikit-learn
- Jupyter Notebook
- Flask / FastAPI
- RESTful APIs
- HTML, CSS, JavaScript
- Leaflet.js
- OpenStreetMap
- Public road accident datasets
- Weather APIs
RoadVision-AI/
│
├── data/ # Raw and processed datasets
├── data_preprocessing/ # Data preprocessing parts
├── feature_selection/ # Feature selection parts
├── mobile_app/ # Flutter based mobile app development
├── models/ # Trained machine learning models
├── notebooks/ # Data analysis & model training notebooks
├── results/ # Final results
├── docs/ # Diagrams, reports, documentation
├── README.md
└── .gitignore
- Uses only public and non-personal datasets
- No tracking or profiling of individual users
- Predictions are probabilistic, not deterministic
- Designed to support road safety awareness, not to assign blame or responsibility
- Route-to-route accident risk comparison
- Real-time traffic data integration
- Explainable AI to highlight key risk factors
- Multi-city and multi-country expansion
Don Dew
Computer Engineering Undergraduate
AI | Intelligent Applications | Web Systems
This project is developed for academic, research, and learning purposes.
License details will be added in future releases.