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 …

Predicting urban region heat via learning arrive-stay-leave behaviors of private cars

Z Xiao, H Li, H Jiang, Y Li, M Alazab… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Urban region heat refers to the extent of which people congregate in various regions when
they travel to and stay in a specified place. Predicting urban region heat facilitates broad …

A survey on modern deep neural network for traffic prediction: Trends, methods and challenges

DA Tedjopurnomo, Z Bao, B Zheng… - … on Knowledge and …, 2020 - ieeexplore.ieee.org
In this modern era, traffic congestion has become a major source of severe negative
economic and environmental impact for urban areas worldwide. One of the most efficient …

Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting

Z Cui, K Henrickson, R Ke… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due
to the time-varying traffic patterns and the complicated spatial dependencies on road …

Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

Y Li, R Yu, C Shahabi, Y Liu - arXiv preprint arXiv:1707.01926, 2017 - arxiv.org
Spatiotemporal forecasting has various applications in neuroscience, climate and
transportation domain. Traffic forecasting is one canonical example of such learning task …

Adaptive graph convolutional recurrent network for traffic forecasting

L Bai, L Yao, C Li, X Wang… - Advances in neural …, 2020 - proceedings.neurips.cc
Modeling complex spatial and temporal correlations in the correlated time series data is
indispensable for understanding the traffic dynamics and predicting the future status of an …

A survey on deep learning for human mobility

M Luca, G Barlacchi, B Lepri… - ACM Computing Surveys …, 2021 - dl.acm.org
The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …

Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction

H Yao, X Tang, H Wei, G Zheng, Z Li - Proceedings of the AAAI …, 2019 - ojs.aaai.org
Traffic prediction has drawn increasing attention in AI research field due to the increasing
availability of large-scale traffic data and its importance in the real world. For example, an …

Traffic flow forecasting with spatial-temporal graph diffusion network

X Zhang, C Huang, Y Xu, L Xia, P Dai, L Bo… - Proceedings of the …, 2021 - ojs.aaai.org
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of
spatial-temporal mining applications, such as intelligent traffic control and public risk …

Brits: Bidirectional recurrent imputation for time series

W Cao, D Wang, J Li, H Zhou… - Advances in neural …, 2018 - proceedings.neurips.cc
Time series are widely used as signals in many classification/regression tasks. It is
ubiquitous that time series contains many missing values. Given multiple correlated time …