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ML Optimization for Traffic Pricing by Lê Hoàng Phương Linh is a document available to read on EtoBox.
What is ML Optimization for Traffic Pricing about?
The paper presents a machine learning (ML)-powered framework designed to optimize path-based congestion pricing policies in urban traffic systems, addressing the limitations of traditional models. By integrating supervised ML with bilevel optimization, the framework significantly improves computational efficiency and scalability, achieving up to 60% better performance in larger networks. Future research is suggested to enhance real-world applicability by incorporating dynamic models and multimodal transport
- Author
- Lê Hoàng Phương Linh
- Language
- EN