[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 review of traffic congestion prediction using artificial intelligence
M Akhtar, S Moridpour - Journal of Advanced Transportation, 2021 - Wiley Online Library
In recent years, traffic congestion prediction has led to a growing research area, especially
of machine learning of artificial intelligence (AI). With the introduction of big data by …
of machine learning of artificial intelligence (AI). With the introduction of big data by …
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
T-GCN: A temporal graph convolutional network for traffic prediction
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic
system and is of great significance for urban traffic planning, traffic management, and traffic …
system and is of great significance for urban traffic planning, traffic management, and traffic …
Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
For intelligent transportation systems (ITS), predicting urban traffic crowd flows is of great
importance. However, it is challenging to represent various complex spatial relationships …
importance. However, it is challenging to represent various complex spatial relationships …
A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
A hybrid CNN-LSTM model for forecasting particulate matter (PM2. 5)
T Li, M Hua, XU Wu - Ieee Access, 2020 - ieeexplore.ieee.org
PM2. 5 is one of the most important pollutants related to air quality, and the increase of its
concentration will aggravate the threat to people's health. Therefore, the prediction of …
concentration will aggravate the threat to people's health. Therefore, the prediction of …
LSTM network: a deep learning approach for short‐term traffic forecast
Short‐term traffic forecast is one of the essential issues in intelligent transportation system.
Accurate forecast result enables commuters make appropriate travel modes, travel routes …
Accurate forecast result enables commuters make appropriate travel modes, travel routes …
[HTML][HTML] A3t-gcn: Attention temporal graph convolutional network for traffic forecasting
J Bai, J Zhu, Y Song, L Zhao, Z Hou, R Du… - … International Journal of …, 2021 - mdpi.com
Accurate real-time traffic forecasting is a core technological problem against the
implementation of the intelligent transportation system. However, it remains challenging …
implementation of the intelligent transportation system. However, it remains challenging …
DeepPF: A deep learning based architecture for metro passenger flow prediction
This study aims to combine the modeling skills of deep learning and the domain knowledge
in transportation into prediction of metro passenger flow. We present an end-to-end deep …
in transportation into prediction of metro passenger flow. We present an end-to-end deep …