发表机构
IIITDM Kurnool; Sardar Vallabhbhai National Institute of Technology Surat（库尔努尔印度信息技术、设计和制造学院; 苏拉特萨达尔·瓦拉巴伊国家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出SA-GNN模型，结合空间聚类与自适应注意力，利用苏拉特移动感知数据预测PM2.5，R²达0.95，优于基线模型，可实现细粒度实时监测与个性化暴露追踪。
AI 中文摘要
城市空气质量沿交通廊道变化显著，因此需要高分辨率监测。本研究引入了来自印度古吉拉特邦苏拉特的新型移动感知数据集，包含PM$_{2.5}$浓度、气象变量（温度、湿度、风速、风向）以及土地利用特征。为将时空数据表示为图，采用了两种节点定义策略：（i）均匀分割（200-400米间隔）和（ii）DBSCAN聚类以自适应分组密集观测数据。针对每个节点，计算了气象变量的滚动均值和标准差。为建模该高维数据，我们提出了SA-GNN（空间注意力图神经网络），用于细粒度短期PM$_{2.5}$预测和热点识别。我们将SA-GNN与LSTM、RNN、GRU和ANN模型进行了比较，这些模型在低分辨率数据上表现良好，但难以捕捉城市空气质量的快速变化模式。SA-GNN采用特定于聚类的GRU来捕获局部时间依赖性，并使用图注意力网络学习空间异质性，这种混合架构能有效建模快速波动和复杂的空间相互作用。在我们的数据集上，SA-GNN取得了$R^2=0.95$、RMSE=6.8、MAE=4.2微克/立方米的结果，优于所有基线模型。将空间聚类与自适应注意力相结合显著提升了预测效果，可实现实时细粒度监测，支持个性化暴露追踪，并为建设更健康的城市提供及时预警。
英文摘要
Urban air quality can vary significantly along transit corridors, necessitating high-resolution monitoring. This work introduces a novel mobile-sensing dataset from Surat, Gujarat, India, comprising PM$*{2.5}$ concentrations, meteorological variables (temperature, humidity, wind speed, wind direction), and land-use features. To represent the spatiotemporal data as a graph, two node-definition strategies were used: (i) uniform segmentation (200--400~m intervals) and (ii) DBSCAN clustering to adaptively group dense observations. For each node, rolling mean and standard deviation of meteorological variables were computed. To model this high-dimensional data, we propose a SA-GNN for fine-grained, short-term PM$*{2.5}$ forecasting and hotspot identification. We compared SA-GNN with LSTM, RNN, GRU, and ANN models. These models performed well on low-resolution data but had difficulty capturing rapidly changing patterns in urban air quality. SA-GNN employs cluster-specific GRUs to capture localized temporal dependencies and a Graph Attention Network to learn spatial heterogeneity. This hybrid architecture effectively models rapid fluctuations and complex spatial interactions. On our dataset, SA-GNN achieved $R^2 = 0.95$, RMSE $= 6.8$, and MAE $= 4.2~\si{\micro\gram\per\meter\cubed}$, outperforming all baseline models. Combining spatial clustering with adaptive attention significantly improves forecasting, enabling real-time, fine-grained monitoring and supporting personalized exposure tracking and timely alerts for healthier cities.
Comments24 Pages, 14 Figures, World Conference of Transport Research2026 Transport Research
