DNN-based prediction model for spatio-temporal data

J Zhang, Y Zheng, D Qi, R Li, X Yi - Proceedings of the 24th ACM …, 2016 - dl.acm.org
Advances in location-acquisition and wireless communication technologies have led to
wider availability of spatio-temporal (ST) data, which has unique spatial properties (ie …

Matrix factorization for spatio-temporal neural networks with applications to urban flow prediction

Z Pan, Z Wang, W Wang, Y Yu, J Zhang… - Proceedings of the 28th …, 2019 - dl.acm.org
Predicting urban flow is essential for city risk assessment and traffic management, which
profoundly impacts people's lives and property. Recently, some deep learning models …

Cross-city transfer learning for deep spatio-temporal prediction

L Wang, X Geng, X Ma, F Liu, Q Yang - arXiv preprint arXiv:1802.00386, 2018 - arxiv.org
Spatio-temporal prediction is a key type of tasks in urban computing, eg, traffic flow and air
quality. Adequate data is usually a prerequisite, especially when deep learning is adopted …

Spatio-temporal recurrent convolutional networks for citywide short-term crowd flows prediction

W Jin, Y Lin, Z Wu, H Wan - … of the 2nd International Conference on …, 2018 - dl.acm.org
With the rapid development of urban traffic, forecasting the flows of crowd plays an
increasingly important role in traffic management and public safety. However, it is very …

[HTML][HTML] Predicting citywide crowd flows using deep spatio-temporal residual networks

J Zhang, Y Zheng, D Qi, R Li, X Yi, T Li - Artificial Intelligence, 2018 - Elsevier
Forecasting the flow of crowds is of great importance to traffic management and public
safety, and very challenging as it is affected by many complex factors, including spatial …

[PDF][PDF] Modeling spatial-temporal dynamics for traffic prediction

H Yao, X Tang, H Wei, G Zheng, Y Yu… - arXiv preprint arXiv …, 2018 - researchgate.net
Spatial-temporal prediction has many applications such as climate forecasting and urban
planning. In particular, traffic prediction has drawn increasing attention in data mining …

Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

A Ali, Y Zhu, M Zakarya - Neural networks, 2022 - Elsevier
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 …

Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis

Z Lin, J Feng, Z Lu, Y Li, D Jin - Proceedings of the AAAI conference on …, 2019 - aaai.org
Crowd flow prediction is of great importance in a wide range of applications from urban
planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds …

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

Y Ren, H Chen, Y Han, T Cheng, Y Zhang… - International Journal of …, 2020 - Taylor & Francis
The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning
(DL) for predicting the volume of citywide spatio-temporal flows. However, this model …

Multisize patched spatial-temporal transformer network for short-and long-term crowd flow prediction

Y Xie, J Niu, Y Zhang, F Ren - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
The prediction of urban crowds is crucial not only to traffic management but also to studies
on the city-level social phenomena, such as energy consumption, urban growth, city …