Smart City Gnosys
Smart city article details
| Title | Fine-Grained Urban Flow Prediction |
|---|---|
| ID_Doc | 26541 |
| Authors | Liang Y.; Ouyang K.; Sun J.; Wang Y.; Zhang J.; Zheng Y.; Rosenblum D.; Zimmermann R. |
| Year | 2021 |
| Published | The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021 |
| DOI | http://dx.doi.org/10.1145/3442381.3449792 |
| Abstract | Urban flow prediction benefits smart cities in many aspects, such as traffic management and risk assessment. However, a critical prerequisite for these benefits is having fine-grained knowledge of the city. Thus, unlike previous works that are limited to coarse-grained data, we extend the horizon of urban flow prediction to fine granularity which raises specific challenges: 1) the predominance of inter-grid transitions observed in fine-grained data makes it more complicated to capture the spatial dependencies among grid cells at a global scale; 2) it is very challenging to learn the impact of external factors (e.g., weather) on a large number of grid cells separately. To address these two challenges, we present a Spatio-Temporal Relation Network (STRN) to predict fine-grained urban flows. First, a backbone network is used to learn high-level representations for each cell. Second, we present a Global Relation Module (GloNet) that captures global spatial dependencies much more efficiently compared to existing methods. Third, we design a Meta Learner that takes external factors and land functions (e.g., POI density) as inputs to produce meta knowledge and boost model performances. We conduct extensive experiments on two real-world datasets. The results show that STRN reduces the errors by 7.1% to 11.5% compared to the state-of-the-art method while using much fewer parameters. Moreover, a cloud-based system called UrbanFlow 3.0 has been deployed to show the practicality of our approach. © 2021 ACM. |
| Author Keywords | Convolution neural networks; Relational learning; Spatio-temporal data; Urban computing.; Urban flow prediction |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
26538  | 0.93 | 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) |
| 26540 | 0.914 | 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) |
| 29131 | 0.912 | Xu Z.; Kang Y.; Cao Y. | High-Resolution Urban Flows Forecasting With Coarse-Grained Spatiotemporal Data | IEEE Transactions on Artificial Intelligence, 4, 2 (2023) |
| 60279 | 0.908 | 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) |
| 52510 | 0.905 | 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) |
| 52833 | 0.902 | Zhao L.; Gao M.; Wang Z. | St-Gsp: Spatial-Temporal Global Semantic Representation Learning For Urban Flow Prediction | WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining (2022) |
| 6377 | 0.901 | 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) |
| 46173 | 0.89 | Ma Q.; Zhang Z.; Zhao X.; Li H.; Zhao H.; Wang Y.; Liu Z.; Wang W. | Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting | International Conference on Information and Knowledge Management, Proceedings (2023) |
| 40011 | 0.889 | Luo G.; Zhang H.; Yuan Q.; Li J.; Wang W.; Wang F.-Y. | One Size Fits All: A Unified Traffic Predictor For Capturing The Essential Spatial-Temporal Dependency | IEEE Transactions on Neural Networks and Learning Systems, 35, 8 (2024) |
| 4890 | 0.888 | Zhang Y.; Zhang F. | A Spatiotemporal Neural Network Model For City Traffic Prediction | Proceedings of SPIE - The International Society for Optical Engineering, 12970 (2023) |