A survey of large language models in transportation planning: modelling, design and decision-making

Large Language Models (LLMs) offer transformative potential for transportation, yet most of the existing review focus on vehicle-level techniques, leaving a gap in understanding the potential of LLM in city-scale and long-term transportation planning. Our survey addresses this gap by systematically analysing 61 studies across problem formulation, solution generation and evaluation – including transport analysis, modelling, system design, and decision-making. We examine existing applications strategies, identifying underexplored areas for leveraging LLMs in large-scale transportation planning. While LLMs excel in transportation analysis and optimisation, system design and decision-making remains untapped. Current applications focus on data processing over reasoning and the lack of multimodal LLM, causal reasoning, and real-time adaptability restricts their role in more complex transportation challenges. Based on these findings, we propose a Cognitive-Adaptive-Interactive framework, offering a structured approach to future LLM applications in transportation and urban systems. Our framework enhances adaptability, efficiency, and decision-making in modern transportation systems.