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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.07158 |
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| _version_ | 1866909619396280320 |
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| author | Da, Longchao Chen, Tiejin Li, Zhuoheng Bachiraju, Shreyas Yao, Huaiyuan Li, Li Dong, Yushun Hu, Xiyang Tu, Zhengzhong Wang, Dongjie Zhao, Yue Zhou, Ben Pendyala, Ram Stabler, Benjamin Yang, Yezhou Zhou, Xuesong Wei, Hua |
| author_facet | Da, Longchao Chen, Tiejin Li, Zhuoheng Bachiraju, Shreyas Yao, Huaiyuan Li, Li Dong, Yushun Hu, Xiyang Tu, Zhengzhong Wang, Dongjie Zhao, Yue Zhou, Ben Pendyala, Ram Stabler, Benjamin Yang, Yezhou Zhou, Xuesong Wei, Hua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI in Transportation Planning: A Survey Da, Longchao Chen, Tiejin Li, Zhuoheng Bachiraju, Shreyas Yao, Huaiyuan Li, Li Dong, Yushun Hu, Xiyang Tu, Zhengzhong Wang, Dongjie Zhao, Yue Zhou, Ben Pendyala, Ram Stabler, Benjamin Yang, Yezhou Zhou, Xuesong Wei, Hua Artificial Intelligence 68T99, 90B06 I.2.6; I.2.8; I.6.3; J.2 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. |
| title | Generative AI in Transportation Planning: A Survey |
| topic | Artificial Intelligence 68T99, 90B06 I.2.6; I.2.8; I.6.3; J.2 |
| url | https://arxiv.org/abs/2503.07158 |