Smart City Gnosys

Smart city article details

Title	Fgitrans: Cross-City Transformer For Fine-Grained Urban Flow Inference
ID_Doc	26432
Authors	Zheng Y.; Cai Y.; Cai Z.; Fan C.; Wang S.; Wang J.
Year	2024
Published	International Conference on Information and Knowledge Management, Proceedings
DOI	http://dx.doi.org/10.1145/3627673.3679855
Abstract	Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. Adequate data is usually a prerequisite for existing machine learning methods, especially most deep learning models. However, many cities still suffer from the data scarcity issue due to the unbalanced city development levels. To mitigate this issue, we propose a novel cross-city fine-grained urban flow inference model named FGITrans, which aims to effectively transfer the knowledge from the data-rich cities to the data-scarce cities. Specifically, we design a weight-sharing triple-branch transformer framework which adopts self-attention and cross-attention for source/target city feature learning and domain alignment, respectively. Then, we propose a novel spatio-temporal adaptive embedding (STAE) layer for our transformer framework, and introduce a cross-city knowledge distillation (CKD) loss to narrow the cross-city disparities. The CKD loss explicitly enforces the framework to learn the discriminative domain-specific and domain-invariant representations simultaneously. Extensive experiments conducted on four large real-world datasets validate the effectiveness of FGITrans compared with the state-of-the-art baselines. © 2024 ACM.
Author Keywords	spatial-temporal data mining; transfer learning; urban flow inference

Similar Articles

Id	Similarity	Authors	Title	Published
6377	0.903	Zheng Y.; Wu J.; Cai Z.; Wang S.; Wang J.	Adatm: Fine-Grained Urban Flow Inference With Adaptive Knowledge Transfer Across Multiple Cities	International Conference on Information and Knowledge Management, Proceedings (2024)
26538	0.896	Ouyang K.; Liang Y.; Liu Y.; Tong Z.; Ruan S.; Zheng Y.; Rosenblum D.S.	Fine-Grained Urban Flow Inference	IEEE Transactions on Knowledge and Data Engineering, 34, 6 (2022)
60279	0.886	Liang Y.; Ouyang K.; Jing L.; Ruan S.; Liu Y.; Zhang J.; Rosenblum D.S.; Zheng Y.	Urbanfm: Inferring Fine-Grained Urban Flows	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)
26541	0.886	Liang Y.; Ouyang K.; Sun J.; Wang Y.; Zhang J.; Zheng Y.; Rosenblum D.; Zimmermann R.	Fine-Grained Urban Flow Prediction	The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021 (2021)
52510	0.88	Xu X.; Wang Z.; Gao Q.; Zhong T.; Hui B.; Zhou F.; Trajcevski G.	Spatial-Temporal Contrasting For Fine-Grained Urban Flow Inference	IEEE Transactions on Big Data, 9, 6 (2023)
60301	0.877	Zhang D.; Chen L.; Zhang L.	Urbanmc: Masking And Contrastive Self-Supervision For Fine-Grained Urban Flows Inference	ACM International Conference Proceeding Series (2024)
19922	0.876	Zheng Y.; Zhong L.; Wang S.; Yang Y.; Gu W.; Zhang J.; Wang J.	Diffuflow: Robust Fine-Grained Urban Flow Inference With Denoising Diffusion Model	International Conference on Information and Knowledge Management, Proceedings (2023)
17869	0.875	Zou X.; Yan Y.; Hao X.; Hu Y.; Wen H.; Liu E.; Zhang J.; Li Y.; Li T.; Zheng Y.; Liang Y.	Deep Learning For Cross-Domain Data Fusion In Urban Computing: Taxonomy, Advances, And Outlook	Information Fusion, 113 (2025)
29131	0.874	Xu Z.; Kang Y.; Cao Y.	High-Resolution Urban Flows Forecasting With Coarse-Grained Spatiotemporal Data	IEEE Transactions on Artificial Intelligence, 4, 2 (2023)
26540	0.872	Li J.; Wang S.; Zhang J.; Miao H.; Zhang J.; Yu P.S.	Fine-Grained Urban Flow Inference With Incomplete Data	IEEE Transactions on Knowledge and Data Engineering, 35, 6 (2023)