Smart cities rely on technology to achieve automated and intelligent urban management, with artif... more Smart cities rely on technology to achieve automated and intelligent urban management, with artificial intelligence (AI) playing a key role. Using Singapore as a case study, this paper proposes a smart city framework that combines multi-source data fusion with deep learning, focusing on traffic flow prediction and urban energy consumption optimization. Based on open data from the Singapore government and information collected from sensor networks, we trained a hybrid model of an improved convolutional neural network (CNN) and a long short-term memory network (LSTM) to model and predict multiple urban indicators. Results show that the system improves accuracy by 12.4% in traffic prediction tasks and saves approximately 8.9% in energy consumption control simulations. This study demonstrates that AI combined with data fusion strategies can effectively improve urban governance efficiency and provides a model for other cities.
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Papers by Minnie Lin