Smart City Gnosys

Smart city article details

Title	A Deep Urban Hotspots Prediction Framework With Modeling Geography-Semantic Dynamics; [顾及地理-语义动态的城市热点预测框架]
ID_Doc	1399
Authors	Sha H.; Jin G.; Cheng G.; Huang J.; Wu K.
Year	2022
Published	Journal of Geo-Information Science, 24, 1
DOI	http://dx.doi.org/10.12082/dqxxkx.2022.210245
Abstract	Urban hotspots prediction is a basic but significant task for future urban management. Accurate urban hotspots prediction can improve the efficiency of urban planning and security construction. Existing deep learning methods mainly adopt geographic grid maps, provided urban network, or external data to capture spatiotemporal dynamics. However, we observe that mining some latent self-semantics from raw data and fusing them with geospatial based grid images can also improve the performance of spatiotemporal predictions. In this paper, we propose Geographic-Semantic Ensemble Neural Network (GSEN), a novel deep learning approach, to stack geographical prediction neural network and semantical prediction neutral network. GSEN model integrates the structures of Predictive Recurrent Neural Network (PredRNN), Graph Convolutional Predictive Recurrent Neural Network (GC-PredRNN), and Ensemble Layer to capture spatiotemporal dynamics from different views. Furthermore, this model can also be correlated with some latent high-level dynamics in the real-world without any external data. We evaluate our proposed model on three different real-world datasets. The experimental results demonstrate the generalization and effectiveness of GSEN in different urban hotspots spatiotemporal prediction tasks. © 2022, Science Press. All right reserved.
Author Keywords	City hotspot; Graph convolutional neural network; network; Predictive recurrent neural; Semantic modeling; Smart city; Spatiotemporal prediction

Similar Articles

Id	Similarity	Authors	Title	Published
60016	0.898	Jin G.; Sha H.; Xi Z.; Huang J.	Urban Hotspot Forecasting Via Automated Spatio-Temporal Information Fusion	Applied Soft Computing, 136 (2023)
52833	0.882	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)
60280	0.875	Li Z.; Xia L.; Tang J.; Xu Y.; Shi L.; Xia L.; Yin D.; Huang C.	Urbangpt: Spatio-Temporal Large Language Models	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2024)
18141	0.871	Zhang C.; Cai L.; Chen M.; Li X.; Cong G.	Deepmeshcity: A Deep Learning Model For Urban Grid Prediction	ACM Transactions on Knowledge Discovery from Data, 18, 6 (2024)
52571	0.858	Wang S.; Miao H.; Li J.; Cao J.	Spatio-Temporal Knowledge Transfer For Urban Crowd Flow Prediction Via Deep Attentive Adaptation Networks	IEEE Transactions on Intelligent Transportation Systems, 23, 5 (2022)
52567	0.858	Jin G.; Liang Y.; Fang Y.; Shao Z.; Huang J.; Zhang J.; Zheng Y.	Spatio-Temporal Graph Neural Networks For Predictive Learning In Urban Computing: A Survey	IEEE Transactions on Knowledge and Data Engineering, 36, 10 (2024)
26541	0.856	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)
4890	0.856	Zhang Y.; Zhang F.	A Spatiotemporal Neural Network Model For City Traffic Prediction	Proceedings of SPIE - The International Society for Optical Engineering, 12970 (2023)
11537	0.854	Li T.; Zhang J.; Bao K.; Liang Y.; Li Y.; Zheng Y.	Autost: Efficient Neural Architecture Search For Spatio-Temporal Prediction	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2020)
59338	0.852	Fang J.; Chen L.; Chai D.; Hong Y.; Xie X.; Chen L.; Wang L.	Uctb: An Urban Computing Tool Box For All-In-One Spatiotemporal Prediction Solution	CCF Transactions on Pervasive Computing and Interaction (2025)