Deep learning on traffic prediction: Methods, analysis, and future directions

X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …

Graph neural network for traffic forecasting: The research progress

W Jiang, J Luo, M He, W Gu - ISPRS International Journal of Geo …, 2023 - mdpi.com
Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …

Temporal attention unit: Towards efficient spatiotemporal predictive learning

C Tan, Z Gao, L Wu, Y Xu, J Xia… - Proceedings of the …, 2023 - openaccess.thecvf.com
Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …

Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

H Peng, H Wang, B Du, MZA Bhuiyan, H Ma, J Liu… - Information …, 2020 - Elsevier
Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as
subway/bus stations, is a practical application and of great significance for urban traffic …

LSTM-based traffic flow prediction with missing data

Y Tian, K Zhang, J Li, X Lin, B Yang - Neurocomputing, 2018 - Elsevier
Traffic flow prediction plays a key role in intelligent transportation systems. However, since
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …

[HTML][HTML] Urban traffic flow prediction techniques: A review

B Medina-Salgado, E Sánchez-DelaCruz… - … Informatics and Systems, 2022 - Elsevier
In recent decades, the development of transport infrastructure has had a great development,
although traffic problems continue to spread due to increase due to the increase in the …

Deep learning for intelligent transportation systems: A survey of emerging trends

M Veres, M Moussa - IEEE Transactions on Intelligent …, 2019 - ieeexplore.ieee.org
Transportation systems operate in a domain that is anything but simple. Many exhibit both
spatial and temporal characteristics, at varying scales, under varying conditions brought on …

Topological graph convolutional network-based urban traffic flow and density prediction

H Qiu, Q Zheng, M Msahli, G Memmi… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
With the development of modern Intelligent Transportation System (ITS), reliable and
efficient transportation information sharing becomes more and more important. Although …

A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing

A Ali, Y Zhu, M Zakarya - Multimedia Tools and Applications, 2021 - Springer
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 …

Deep multi-view spatial-temporal network for taxi demand prediction

H Yao, F Wu, J Ke, X Tang, Y Jia, S Lu… - Proceedings of the …, 2018 - ojs.aaai.org
Taxi demand prediction is an important building block to enabling intelligent transportation
systems in a smart city. An accurate prediction model can help the city pre-allocate …