Can LLM-Based Agents Transform Transportation Planning?

In this episode, we explore how Large Language Models (LLMs) are reshaping the future of transportation planning. What happens when urban mobility is no longer modeled through rigid assumptions, but through agents capable of human-like reasoning and decision-making?We dive into the shift from traditional models to behaviorally rich, AI-driven simulations, examining how LLM-based agents can better capture the complexity of travel behavior. From land use–transport interactions to scenario-based policy testing, this approach opens new possibilities for more flexible, data-efficient, and realistic planning practices.What does this mean for the future of cities? Can AI-driven models truly enhance decision-making, or do they introduce new challenges for planners?This episode is an AI-narrated version of the original blog post published on the UrbanWise website.Curated from the UrbanWise platform founded by Melike Şenkal, this podcast transforms insightful urban planning content from blog to audio, for curious minds and future-focused thinkers on the move.🌐 Read more at ⁠UrbanWise⁠ and subscribe for more UrbanWise Voices.