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Smart city article details

Title	Urban Emergency Response Optimization Algorithm Based On Deep Learning
ID_Doc	59941
Authors	Luo L.; Qi X.; Wang C.
Year	2024
Published	Proceedings - 2024 International Conference on Computers, Information Processing and Advanced Education, CIPAE 2024
DOI	http://dx.doi.org/10.1109/CIPAE64326.2024.00062
Abstract	Effective and rapid emergence approaches and responses remain critical in minimizing the impacts of accidents and disasters in the modern urban environment. This article, therefore, draws attention to optimization algorithms that are effectively used in urban emergency response. These efforts will play a fundamental role in leveraging their capabilities through deep learning. These algorithms are primarily designed to analyze a significant amount of urban emergency data from historical incident data, traffic patterns, and population densities—to predict and optimize emergency response approaches. This article examines the integration of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in the temporal and spatial data analysis process. The experimental results reveal improvements in resource allocation and response times in rapid emergencies compared to the traditional models. The examined model not only plays a critical role in enhancing the accuracy and speed of emergency response but also adapts to the significantly changing conditions within urban settings, providing an effective and robust solution to emergency management in emerging smart cities. © 2024 IEEE.
Author Keywords	Automated Systems; Deep Learning Systems; Disaster Management Systems; Optimization Algorithms; Urban Emergency Response

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