Spatiotemporal Graph Networks for Relational Reasoning in Campus Infrastructure Management

The efficient management of campus infrastructure presents a complex spatiotemporal forecasting challenge characterized by dynamic interdependencies between physical assets. Traditional models fail to capture these intricate relationships as they treat buildings as independent entities or rely on static correlation structures. This paper introduces a novel Spatiotemporal Graph Neural Network (ST-GNN) framework that reframes infrastructure forecasting as a relational reasoning task, enabling dynamic inference of campus wide interdependencies. Our approach integrates Graph Attention Networks (GAT) to learn time-varying spatial dependencies and Gated Temporal Convolutional Networks (TCNs) to capture multi-scale temporal patterns. A key innovation is our context-sensitive graph construction method that incorporates physical proximity, functional similarity, and human mobility data to create a holistic representation of campus dynamics. Evaluated on a real-world multimodal dataset comprising 24 months of energy and occupancy data from 50 campus buildings, the proposed model demonstrates superior performance, achieving a 16.3% reduction in mean absolute error compared to the strongest baseline. Comprehensive ablation studies confirm the critical contribution of each architectural component, while qualitative analysis reveals the model’s capacity to provide interpretable insights into campus operational patterns. This work provides a powerful framework for intelligent campus management, enabling precise resource allocation, energy optimization, and sustainable operational planning through advanced relational reasoning capabilities.