In recent years, smart and connected urban infrastructures have undergone a fast expansion, which increasingly generates huge amounts of urban big data, such as human mobility data, location-based transaction data, regional weather and air quality data, social connection data. These heterogeneous data sources convey rich information about the city and can be naturally linked with or modeled by graphs, e.g., urban social graph, transportation graph. These urban graph data can enable intelligent solutions to solve various urban challenges, such as urban facility planning, air pollution, etc. However, it is also very challenging to manage, analyze, and make sense of such big urban graph data. Recently, there have been many studies on advancing and expanding Graph Neural Networks (GNNs) approaches for various urban intelligence applications. In this chapter, we provide a comprehensive overview of the graph neural network (GNN) techniques that have been used to empower urban intelligence, in four application categories, namely, (i) urban anomaly and event detection, (ii) urban configuration and transportation planning, (iii) urban traffic prediction, and (iv) urban human behavior inference. The chapter also discusses future directions of this line of research. The chapter is (tentatively) organized as follows.
- Application scenarios in urban intelligence
- Representing urban systems as graphs
- Case Study: GNN in urban configuration and transportation
- Case Study: GNN in urban anomaly and event detection
- Case Study: GNN in urban human behavior inference
- Future Directions
author = "Li, Yanhua and Zhou, Xun and Pan, Menghai",
editor = "Wu, Lingfei and Cui, Peng and Pei, Jian and Zhao, Liang",
title = "Graph Neural Networks in Urban Intelligence",
booktitle = "Graph Neural Networks: Foundations, Frontiers, and Applications",
year = "2022",
publisher = "Springer Singapore",
address = "Singapore",
pages = "579--593",
Y. Li, X. Zhou, and M. Pan, “Graph neural networks in urban intelligence,” in Graph Neural Networks: Foundations, Frontiers, and Applications, L. Wu, P. Cui, J. Pei, and L. Zhao, Eds. Singapore: Springer Singapore, 2022, pp. 579–593.