Urban Spatial Temporal Prediction

Urban spatial-temporal prediction focuses on forecasting future urban phenomena, like traffic congestion or resource demand, by analyzing spatiotemporal data patterns. Current research emphasizes developing universal models capable of handling diverse data types and prediction tasks, often leveraging techniques inspired by large language models and addressing challenges like concept drift through adaptive mechanisms. This field is crucial for optimizing urban resource allocation, improving emergency response, and enhancing overall city management, driving the development of standardized datasets and open-source libraries to facilitate reproducible research and collaborative advancements.