[HTML][HTML] Unsupervised machine learning in urban studies: A systematic review of applications
J Wang, F Biljecki - Cities, 2022 - Elsevier
Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …
[HTML][HTML] A survey of traffic prediction: from spatio-temporal data to intelligent transportation
Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …
efficient. With the development of mobile Internet and position technologies, it is reasonable …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Privacy-preserved data sharing towards multiple parties in industrial IoTs
The effective physical data sharing has been facilitating the functionality of Industrial IoTs,
which is believed to be one primary basis for Industry 4.0. These physical data, while …
which is believed to be one primary basis for Industry 4.0. These physical data, while …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
A hybrid-convolution spatial–temporal recurrent network for traffic flow prediction
X Zhang, S Wen, L Yan, J Feng, Y Xia - The Computer Journal, 2024 - academic.oup.com
Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …
Urban traffic prediction from spatio-temporal data using deep meta learning
Predicting urban traffic is of great importance to intelligent transportation systems and public
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
A survey on mobile crowdsensing systems: Challenges, solutions, and opportunities
Mobile crowdsensing (MCS) has gained significant attention in recent years and has
become an appealing paradigm for urban sensing. For data collection, MCS systems rely on …
become an appealing paradigm for urban sensing. For data collection, MCS systems rely on …
A private and efficient mechanism for data uploading in smart cyber-physical systems
To provide fine-grained access to different dimensions of the physical world, the data
uploading in smart cyber-physical systems suffers novel challenges on both energy …
uploading in smart cyber-physical systems suffers novel challenges on both energy …