Generative AI in Transportation Planning: A Survey
Longchao Da, Tiejin Chen, Zhuoheng Li, Shreyas Bachiraju, Huaiyuan Yao, Li Li, Yushun Dong, Xiyang Hu, Zhengzhong Tu, Dongjie Wang, Yue Zhao, Ben Zhou, Ram Pendyala, Benjamin Stabler, Yezhou Yang, Xuesong Zhou, Hua Wei
TL;DR
This survey defines a principled GenAI framework for transportation planning, introducing a two-perspective taxonomy that links transport tasks with computational techniques and detailing data preparation, fine-tuning, and inference approaches. It covers core model families (GANs, VAEs, diffusion models, LLMs, and MLLMs), key applications (scenario generation, demand forecasting, and traffic simulation), and practical pipelines including retrieval-augmented generation and few-shot learning. A substantive OD-calibration case study demonstrates how domain-focused fine-tuning and prompt design can outperform traditional optimization methods, while highlighting limitations and the need for domain knowledge. The work emphasizes trust, explainability, data scarcity, and equitable deployment, offering a roadmap of modular pipelines, novel evaluation criteria, and collaborative pathways for integrating GenAI into transportation planning. Overall, it provides a forward-looking blueprint for leveraging GenAI to improve mobility, resilience, and sustainability across diverse urban contexts.
Abstract
The integration of generative artificial intelligence (GenAI) into transportation planning has the potential to revolutionize tasks such as demand forecasting, infrastructure design, policy evaluation, and traffic simulation. However, there is a critical need for a systematic framework to guide the adoption of GenAI in this interdisciplinary domain. In this survey, we, a multidisciplinary team of researchers spanning computer science and transportation engineering, present the first comprehensive framework for leveraging GenAI in transportation planning. Specifically, we introduce a new taxonomy that categorizes existing applications and methodologies into two perspectives: transportation planning tasks and computational techniques. From the transportation planning perspective, we examine the role of GenAI in automating descriptive, predictive, generative, simulation, and explainable tasks to enhance mobility systems. From the computational perspective, we detail advancements in data preparation, domain-specific fine-tuning, and inference strategies, such as retrieval-augmented generation and zero-shot learning tailored to transportation applications. Additionally, we address critical challenges, including data scarcity, explainability, bias mitigation, and the development of domain-specific evaluation frameworks that align with transportation goals like sustainability, equity, and system efficiency. This survey aims to bridge the gap between traditional transportation planning methodologies and modern AI techniques, fostering collaboration and innovation. By addressing these challenges and opportunities, we seek to inspire future research that ensures ethical, equitable, and impactful use of generative AI in transportation planning.