A scalable and generic framework for city-wide traffic prediction with large language model

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 are 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.