Abstract With the advancement of ubiquitous connectivity and autonomous intelligence, future urban transportation systems are expected to become increasingly integrated across modes and contexts, necessitating a scalable and generic city-wide urban traffic prediction framework for traffic management. However, most existing studies focus on traffic flow prediction for a specific transport mode under a single scenario or time granularity, limiting their ability to generalize across diverse transport modes and scenarios. To solve these challenges, we propose a large language model (LLM)-based scalable and generic city-wide urban traffic prediction framework (LLM-UTP) for short-term traffic flow prediction. It captures the generic trends and specific fluctuations across different transport modes and scenarios and consists of three parts: a trend data enhancement module, a spatiotemporal feature encoding module, and an LLM module. Extensive experiments conducted on 11 large-scale real-world traffic datasets from 29 cities/areas covering a wide range of transport modes, traffic scenarios, and time granularities to validate the proposed model’s complexity, scaling law, scalability, generality, and predictive performance, demonstrating its superiority. These results highlight the practical potential of LLM-UTP as a scalable and generic foundation model for intelligent traffic management and decision-making in future smart cities. Similar content being viewed by others Acknowledgements We thank Jinsong Ji and Yongjie Yang for their assistance with figure plotting. Funding J.L.Z., L.X.Y., and Z.Y.G. disclose support for this work. L.X.Y. and Z.Y.G. received support from the National Natural Science Foundation of China [grant number 72288101]. J.L.Z. received support from the National Natural Science Foundation of China [grant numbers 72431002 and 72201029] and the Beijing Natural Science Foundation [grant numbers 4252049 and L251030]. L.X.Y. also received support from the Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China [grant number JYB2025XDXM211]. All other authors declare no relevant funding. Author information Authors and Affiliations Corresponding