Reinforcement Learning to Generative AI: Transforming Transportation Engineering
Year: 2025
Type: article
Abstract: : This paper provides a comprehensive survey on the role of Generative Artificial Intelligence (GenAI) in transportation planning and related applications. The study emphasizes how GenAI techniques, such as large language models and generative design algorithms, can support tasks like demand forecasting, infrastructure design, and traffic simulation. The authors present a framework that connects GenAI capabilities with different stages of transportation planning, highlighting the potential to enhance creativity, efficiency, and adaptability in decisionmaking. Key challenges such as data scarcity, bias in models, interpretability and security issues are critically discussed, with recommendations for overcoming them. The paper also outlines practical examples where GenAI can be integrated into existing transportation systems and policy planning. By combining technical review with application scenarios, the study identifies both the transformative potential of GenAI and the risks that must be managed to ensure reliable outcomes. Overall, this research shows that generative AI can bring significant advancements to urban mobility planning, sustainable transport solutions, and intelligent infrastructure development, making it a key.
Author: Sharma, Dr. Atul Kumar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7108330009
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