2019
DOI: 10.1109/tetci.2019.2907718
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A Survey on an Emerging Area: Deep Learning for Smart City Data

Abstract: Rapid urbanisation has brought about great challenges to our daily lives, such as traffic congestion, environmental pollution, energy consumption, public safety and so on. Research on smart cities aims to address these issues with various technologies developed for the Internet of Things. Very recently, the research focus has shifted towards processing of massive amount of data continuously generated within a city environment, e.g., physical and participatory sensing data on traffic flow, air quality, and heal… Show more

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Cited by 124 publications
(49 citation statements)
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“…As the latest paradigm in computational intelligence, the DNL has demonstrated greater potential over traditional machine learning methods and thus has attracted substantial attention [15]. It can model extremely sophisticated functions and can discover intricate structures from natural data in its raw forms through multiple levels of abstraction and non-linear processing layers trainable from the beginning to the end.…”
Section: Introductionmentioning
confidence: 99%
“…As the latest paradigm in computational intelligence, the DNL has demonstrated greater potential over traditional machine learning methods and thus has attracted substantial attention [15]. It can model extremely sophisticated functions and can discover intricate structures from natural data in its raw forms through multiple levels of abstraction and non-linear processing layers trainable from the beginning to the end.…”
Section: Introductionmentioning
confidence: 99%
“…With the increase in the number of successful cases of application of deep learning in real life, such as in autonomous driving, healthcare, and smart cities [1][2][3][4][5][6][7][8][9], various attempts have been made to apply deep learning to weather-related fields using numerical models [10] to improve the performance of weather forecasting [11][12][13][14][15]. In the field of meteorology, nowcasting is a popular research topic in which deep learning techniques are being actively applied to the analysis of spatiotemporal data, such as radar and satellite data [16][17][18][19].…”
Section: Introductionmentioning
confidence: 99%
“…A seminal method is DeepTTE [21], which uses historical trajectories as training data and predicts the ETA of a given trajectory. DeepTTE is a hybrid model [3] that integrates recurrent neural network and convolutional neural network (CNN) models. It has been cited by many researchers, used for comparison, and extended.…”
Section: Introductionmentioning
confidence: 99%