mlsanityData to 14 Oct 2026

Generative AI in Transportation Planning: A Survey

arXiv 2503.07158 ↗First posted 10 Mar 2025cs.AIcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 83 of its 302 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

83 of 302 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.

  1. 2025LangCoop: Collaborative Driving with Languagecited by 25
  2. 2026The Autonomy Tax: Defense Training Breaks LLM Agentscited by 9
  3. 2025Generative AI for Autonomous Driving: Frontiers and Opportunitiescited by 7
  4. 2025LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarioscited by 7
  5. 2025DeepShade: Enable Shade Simulation by Text-conditioned Image Generationcited by 4
  6. 2026ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Societycited by 0
  7. 2026Broadening Access to Transportation Safety Data with Generative AI: A Schema-Grounded Framework for Spatial Natural Language Queriescited by 0
  8. 2026MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generationcited by 0

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