[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 …
A survey of traffic prediction: from spatio-temporal data to intelligent transportation
H Yuan, G Li - Data Science and Engineering, 2021 - Springer
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 …
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 …
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 …
Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
We all depend on mobility, and vehicular transportation affects the daily lives of most of us.
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
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 …
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 …
Future directions in human mobility science
We provide a brief review of human mobility science and present three key areas where we
expect to see substantial advancements. We start from the mind and discuss the need to …
expect to see substantial advancements. We start from the mind and discuss the need to …
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 …
Difftraj: Generating gps trajectory with diffusion probabilistic model
Pervasive integration of GPS-enabled devices and data acquisition technologies has led to
an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …
an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …