Generative AI in Transportation Planning: A Survey

• URL: http://arxiv.org/abs/2503.07158v4
• Date: Tue, 18 Mar 2025 05:03:23 GMT
• Title: Generative AI in Transportation Planning: A Survey
• Authors: Longchao Da, Tiejin Chen, Zhuoheng Li, Shreyas Bachiraju, Huaiyuan Yao, Li Li, Yushun Dong, Xiyang Hu, Zhengzhong Tu, Dongjie Wang, Yue Zhao, Xuanyu, Zhou, Ram Pendyala, Benjamin Stabler, Yezhou Yang, Xuesong Zhou, Hua Wei,
• Abstract summary: We present the first comprehensive framework for leveraging GenAI in transportation planning.<n>From the transportation planning perspective, we examine the role of GenAI in automating descriptive, predictive, generative, simulation, and explainable tasks.<n>We address critical challenges, including data scarcity, explainability, bias mitigation, and the development of domain-specific evaluation frameworks.
• Score: 50.88844036728445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior [0.0]
  This paper systematically explores the advancements in adaptive trip route planning and travel time estimation through Artificial Intelligence (AI) established AI techniques, including Machine Learning (ML), Reinforcement Learning (RL), and Graph Neural Networks (GNNs) The paper concludes with recommendations for leveraging AI to build efficient, transparent, and sustainable navigation systems.
  arXiv  Detail & Related papers  (2025-03-30T15:41:44Z)
• Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap [51.198001060683296]
  Large Language Models (LLMs) offer transformative potential to address transportation challenges. This survey first presents LLM4TR, a novel conceptual framework that systematically categorizes the roles of LLMs in transportation. For each role, our review spans diverse applications, from traffic prediction and autonomous driving to safety analytics and urban mobility optimization.
  arXiv  Detail & Related papers  (2025-03-27T11:56:27Z)
• A Survey of World Models for Autonomous Driving [63.33363128964687]
  Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling. This paper systematically reviews recent advances in world models for autonomous driving.
  arXiv  Detail & Related papers  (2025-01-20T04:00:02Z)
• Beyond Words: Evaluating Large Language Models in Transportation Planning [0.0]
  This study investigates the evaluation of Large Language Models (LLMs), specifically GPT-4 and Phi-3-mini, to enhance transportation planning. The findings underscore the transformative potential of GenAI technologies in urban transportation planning.
  arXiv  Detail & Related papers  (2024-09-22T16:20:00Z)
• Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI [0.0]
  This report investigates the history and impact of Generative Models and Connected and Automated Vehicles (CAVs) By focusing on the application of generative models within the context of CAVs, the study aims to unravel how this integration could enhance predictive modeling, simulation accuracy, and decision-making processes in autonomous vehicles.
  arXiv  Detail & Related papers  (2024-03-14T06:51:26Z)
• Automated Process Planning Based on a Semantic Capability Model and SMT [50.76251195257306]
  In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. We present an approach that combines these two topics: starting from a semantic capability model, an AI planning problem is automatically generated.
  arXiv  Detail & Related papers  (2023-12-14T10:37:34Z)
• A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective [7.770651543578893]
  We introduce the principles of different generative AI techniques. We classify tasks in ITS into four types: traffic perception, traffic prediction, traffic simulation, and traffic decision-making. We illustrate how generative AI techniques addresses key issues in these four different types of tasks.
  arXiv  Detail & Related papers  (2023-12-13T16:13:23Z)
• The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review [43.30610493968783]
  We review state-of-the-art deep learning-based planning systems, and focus on how they integrate prediction. We discuss the implications, strengths, and limitations of different integration principles.
  arXiv  Detail & Related papers  (2023-08-10T17:53:03Z)
• Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning [27.549492913085597]
  The two fields of urban planning and artificial intelligence arose and developed separately. There is now cross-pollination and increasing interest in both fields to benefit from the advances of the other.
  arXiv  Detail & Related papers  (2023-04-08T02:19:59Z)
• Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis [71.2468615993246]
  Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness.
  arXiv  Detail & Related papers  (2022-10-18T17:28:58Z)
• Towards Automated Process Planning and Mining [77.34726150561087]
  We present a research project in which researchers from the AI and BPM field work jointly together. We discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models.
  arXiv  Detail & Related papers  (2022-08-18T16:41:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.