Generative artificial intelligence and large language models in transport planning: a narrative review

Generative artificial intelligence and large language models are moving from general-purpose tools into the technical core of transport planning. This narrative review synthesizes the evidence published on their applications, contributions, and limitations. Across the axes the literature converges on the capacity of these models to reproduce activity and travel patterns, code open-ended mobility text, and support simulation and routing at a fraction of conventional cost, while it diverges on their numerical reliability, demographic representativeness, and readiness for decisions with public consequences. The review concludes that generative models are best understood as components of hybrid, human-supervised planning workflows rather than as replacements for behavioral models or deliberative processes, and it proposes a research agenda centered on transport-specific benchmarks, bias auditing, and reproducible evaluation.