https://github.com/rohankumawat/trafficpredictionai
This project leverages the UK Road Traffic Statistics dataset to develop a GeoAI-powered traffic analysis and prediction tool. The application uses machine learning models to predict future traffic patterns based on historical data and integrates OpenAI's GPT-4 API to automate the generation of traffic insights.
https://github.com/rohankumawat/trafficpredictionai
Last synced: about 2 months ago
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This project leverages the UK Road Traffic Statistics dataset to develop a GeoAI-powered traffic analysis and prediction tool. The application uses machine learning models to predict future traffic patterns based on historical data and integrates OpenAI's GPT-4 API to automate the generation of traffic insights.
- Host: GitHub
- URL: https://github.com/rohankumawat/trafficpredictionai
- Owner: rohankumawat
- Created: 2024-09-10T11:00:21.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-09-10T11:06:25.000Z (8 months ago)
- Last Synced: 2025-01-17T08:30:28.587Z (3 months ago)
- Size: 3.91 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# trafficPredictionAI
This project leverages the UK Road Traffic Statistics dataset to develop a GeoAI-powered traffic analysis and prediction tool. The application uses machine learning models to predict future traffic patterns based on historical data and integrates OpenAI's GPT-4 API to automate the generation of traffic insights.
# ToDo List for GeoAI-Traffic-Prediction
## 1. Setup Project Structure
- [ ] Create the GitHub repository with a clear project description
- [ ] Set up the initial project directory as per the project structure
- [ ] Create `.gitignore` file to avoid committing sensitive data (e.g., API keys)
## 2. Data Ingestion and Preprocessing
- [ ] Download UK Road Traffic Statistics dataset
- [ ] Create `data_processing.py` to clean, preprocess, and aggregate traffic data
- [ ] Perform exploratory data analysis (EDA) in `data_exploration.ipynb`
- [ ] Store the processed data in `data/processed/` folder
## 3. Predictive Modeling
- [ ] Build a traffic prediction model using historical traffic data
- [ ] Test different machine learning models (e.g., linear regression, time series)
- [ ] Store trained models and checkpoints in `models/` folder
- [ ] Log training metrics and results with MLflow (`mlflow_logging.py`)
## 4. GPT-4 Integration for Insights
- [ ] Set up OpenAI API in `gpt_integration.py` for automated traffic insights
- [ ] Create prompts for GPT-4 to generate human-readable insights based on traffic data
- [ ] Implement GPT integration to generate insights based on model predictions
## 5. Build the Dashboard
- [ ] Build a Streamlit or Flask app in `app.py` to display traffic predictions
- [ ] Implement file upload feature for users to input traffic data
- [ ] Display data trends and predictions using charts and maps (e.g., road network)
- [ ] Integrate GPT-generated insights into the dashboard
## 6. MLOps (MLflow) Integration
- [ ] Set up MLflow for experiment tracking (model parameters, metrics, logs)
- [ ] Log different versions of models and predictions for reproducibility
- [ ] Save trained models and results for further use or deployment
## 7. Testing and Deployment
- [ ] Test the dashboard functionality with sample traffic data
- [ ] Ensure accurate predictions and meaningful GPT-4 insights
- [ ] Deploy the dashboard locally or to a cloud platform (e.g., Heroku, AWS)
## 8. Documentation
- [ ] Write a detailed `README.md` explaining the project, setup instructions, and usage
- [ ] Document the API configuration and model architecture
- [ ] Create usage instructions for the dashboard, including input formats and outputs
## 9. Finalize and Push to GitHub
- [ ] Push all project files, scripts, and documentation to GitHub
- [ ] Ensure the project is well-documented and ready for public access
- [ ] Add a license file (MIT or GPL) to the repository