Vision-LLMs for Spatiotemporal Traffic Forecasting

ArXi:2510.11282v2 Announce Type: replace Accurate spatiotemporal traffic forecasting is a critical prerequisite for proactive resource management in dense urban mobile networks. While large language models have shown promise in time series analysis, they inherently struggle to model the complex spatial dependencies of grid-based traffic data. Effectively extending large language models to this domain is challenging, as representing the vast amount of information from dense geographical grids can be inefficient and overwhelm the model's context.