Smart Transfer Learning for AI-Powered Urban Mobility
Reinforcement Learning-Driven Transfer Learning in Spatial-Temporal Graph Neural Networks
🔍 The Challenge: AI Models Struggle in Data-Scarce Cities
Many cities lack the massive datasets required for traditional AI models to optimize public transit. Our solution allows AI models to learn from one city and efficiently transfer knowledge to another!
🌟 Our Breakthrough: AI That Learns and Adapts Across Cities!
- ✅ Trained on large cities, applied to smaller ones
- ✅ Uses reinforcement learning for efficient knowledge transfer
- ✅ Boosts accuracy while reducing data dependency
🧠 How It Works: AI + Reinforcement Learning = Smarter Cities
Instead of training models from scratch for every city, we optimize knowledge transfer using:
- 🔹 Graph Neural Networks (GNNs): Understands transit network structures
- 🔹 Reinforcement Learning: Optimizes knowledge transfer efficiency
- 🔹 Scalability: Works on cities with minimal data
📈 Performance & Real-World Validation
- 🚇 Successfully applied to bus networks in Laval, Canada & Ames, USA
- 🌍 Scalable for global urban transit systems
🔗 Want to Collaborate?
📄 Interested in this Project? → Send me an Email
🚀 Join us in transforming the future of intelligent search!