AeonRoute is a sophisticated, hyper-intelligent routing engine designed specifically for Electric Vehicles (EVs) in complex urban environments. By merging Graph Neural Networks (GNNs) with Generative Adversarial Networks (GANs) and Reinforcement Learning (Q-Learning), AeonRoute doesn't just find the shortest path—it predicts traffic dynamics and optimizes for energy efficiency and charging infrastructure availability.
In the transition to sustainable urban mobility, standard routing algorithms (like A* or Dijkstra) fall short. They ignore the non-linear energy consumption of EVs and the stochastic nature of city traffic. AeonRoute addresses this by:
- Synthesizing Realistic Traffic: Using GANs to generate high-fidelity traffic patterns based on historical data.
- Topological Intelligence: Leveraging GNNs to understand the relationship between road segments and charging nodes.
- Adaptive Learning: Utilizing Q-Learning agents that "live" in the simulation, learning the most efficient routes through trial and error in a digital twin of Hyderabad.
AeonRoute is built as a modular microservices-oriented system:
- Hybrid Routing Engine: Orchestrates multiple strategies, from classic graph search to neural-guided pathfinding.
- Spatial Indexing: High-performance spatial lookups for charging stations and road segments.
- Training Pipeline: Integrated services for GNN-GAN training and Q-Table updates.
- Analytics Service: Real-time processing of simulation metrics and health data.
- Dynamic Map Visualization: Real-time rendering of routes and station status.
- Simulation Dashboard: Interactive controls for managing EV agents and viewing hardware-accelerated telemetry.
- Training Monitor: Live SSE (Server-Sent Events) streams for tracking model convergence.
| Category | Technologies |
|---|---|
| Backend | Python 3.10+, FastAPI, NetworkX, GeoPandas, Keras/TensorFlow |
| Frontend | TypeScript, React, Vite, TailwindCSS, Framer Motion, Lucide React |
| Infrastructure | Docker, Docker Compose, Make |
| Data | GraphML (Hyderabad OpenStreetMap Data), JSON |
- Python 3.10 or higher
- Node.js 18+ & npm
- Docker (optional, for containerized deployment)
- Clone the repository:
git clone https://github.com/yourusername/aeonroute.git cd aeonroute - Create and activate a virtual environment:
python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate
- Install dependencies:
pip install -r requirements.txt
- Start the API:
python backend_api.py
- Navigate to the frontend directory:
cd frontend - Install dependencies:
npm install
- Start the development server:
npm run dev
AeonRoute utilizes three primary AI components located in src/models/:
- GNN-GAN: Generates synthetic but realistic traffic flows across the road graph.
- Q-Learning Agent: Optimizes route selection based on a reward function that balances time, energy, and battery safety.
- Spatial Index: Ensures sub-millisecond lookups for nearest charging stations.
To retrain the models:
python src/main.py --trainWe maintain high standards for reliability. Run the test suite using:
# Backend tests
pytest tests/
# Frontend tests
cd frontend && npm testThis project is licensed under the MIT License - see the LICENSE file for details.
Developed with a focus on sustainable urban infrastructure and next-generation AI.