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.

翻译：生成式人工智能（GenAI）与交通规划的融合，有望彻底变革需求预测、基础设施设计、政策评估和交通仿真等任务。然而，在这一跨学科领域中，迫切需要一套系统性的框架来指导GenAI的采用。在本综述中，我们——一个横跨计算机科学与交通工程学的多学科研究团队——提出了首个在交通规划中利用GenAI的综合性框架。具体而言，我们引入了一种新的分类法，将现有应用与方法论划分为两个视角：交通规划任务与计算技术。从交通规划视角，我们审视了GenAI在自动化描述性、预测性、生成性、仿真性和可解释性任务中的作用，以提升交通系统的效能。从计算视角，我们详细阐述了数据准备、领域特定微调以及推理策略（如针对交通应用定制的检索增强生成和零样本学习）方面的进展。此外，我们探讨了关键挑战，包括数据稀缺性、可解释性、偏差缓解，以及开发与可持续性、公平性和系统效率等交通目标相一致的领域特定评估框架。本综述旨在弥合传统交通规划方法论与现代人工智能技术之间的鸿沟，促进合作与创新。通过应对这些挑战与机遇，我们期望激发未来的研究，确保生成式人工智能在交通规划中得到合乎伦理、公平且富有成效的应用。