Smart City Gnosys

Smart city article details

Title	Urban Spatiotemporal Event Prediction Using Convolutional Neural Network And Road Feature Fusion Network
ID_Doc	60194
Authors	Jiang Y.; Zhao S.; Li H.; Wu H.; Zhu W.
Year	2024
Published	ISPRS International Journal of Geo-Information, 13, 10
DOI	http://dx.doi.org/10.3390/ijgi13100341
Abstract	The security challenges faced by smart cities are attracting more attention from more people. Criminal activities and disasters can have a significant impact on the stability of a city, resulting in a loss of safety and property for its residents. Therefore, predicting the occurrence of urban events in advance is of utmost importance. However, current methods fail to consider the impact of road information on the distribution of cases and the fusion of information at different scales. In order to solve the above problems, an urban spatiotemporal event prediction method based on a convolutional neural network (CNN) and road feature fusion network (FFN) named CNN-rFFN is proposed in this paper. The method is divided into two stages: The first stage constructs feature map and structure of CNN then selects the optimal feature map and number of CNN layers. The second stage extracts urban road network information using multiscale convolution and incorporates the extracted road network feature information into the CNN. Some comparison experiments are conducted on the 2018–2019 urban patrol events dataset in Zhengzhou City, China. The CNN-rFFN method has an R2 value of 0.9430, which is higher than the CNN, CNN-LSTM, Dilated-CNN, ResNet, and ST-ResNet algorithms. The experimental results demonstrate that the CNN-rFFN method has better performance than other methods. © 2024 by the authors.
Author Keywords	convolutional neural network; road feature fusion network; smart cities; urban spatiotemporal event

Similar Articles

Id	Similarity	Authors	Title	Published
52587	0.883	Priya K.; Omkar K.; Naresh R.; Venkatesh K.	Spatio-Temporal Traffic Prediction In Urban Area Using Convolutional Temporal Network System	6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings (2025)
52608	0.878	Wu S.	Spatiotemporal Dynamic Forecasting And Analysis Of Regional Traffic Flow In Urban Road Networks Using Deep Learning Convolutional Neural Network	IEEE Transactions on Intelligent Transportation Systems, 23, 2 (2022)
52517	0.869	Huang X.; Pan Z.; Zhao G.	Spatial-Temporal Interactive Graph Convolutional Networks For Traffic Forecasting	2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024 (2024)
29827	0.867	Chen B.; Hu K.; Li Y.; Miao L.	Hybrid Spatio-Temporal Graph Convolution Network For Short-Term Traffic Forecasting	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2022-October (2022)
60735	0.866	Kumari M.; Ulmas Z.; Suseendra R.; Ramesh J.V.N.; El-Ebiary Y.A.B., Prof.	Utilizing Federated Learning For Enhanced Real-Time Traffic Prediction In Smart Urban Environments	International Journal of Advanced Computer Science and Applications, 15, 2 (2024)
4890	0.865	Zhang Y.; Zhang F.	A Spatiotemporal Neural Network Model For City Traffic Prediction	Proceedings of SPIE - The International Society for Optical Engineering, 12970 (2023)
9007	0.865	Xu X.; Mao H.; Zhao Y.; Lü X.	An Urban Traffic Flow Fusion Network Based On A Causal Spatiotemporal Graph Convolution Network	Applied Sciences (Switzerland), 12, 14 (2022)
58588	0.864	Wang X.	Traffic Flow Prediction Method Based On Deep Learning And Graph Neural Network	International Journal of High Speed Electronics and Systems (2025)
17980	0.864	Bharaty K.S.; Konduri P.S.R.	Deep Learning-Driven Smart Signal Systems For Advanced Image And Video Processing In Urban Infrastructure	Proceedings - 4th International Conference on Smart Technologies, Communication and Robotics 2025, STCR 2025 (2025)
4883	0.864	Cao S.; Wu L.; Wu J.; Wu D.; Li Q.	A Spatio-Temporal Sequence-To-Sequence Network For Traffic Flow Prediction	Information Sciences, 610 (2022)