Modeling Dynamic Spatio-Temporal Correlations for Urban Traffic Flows Prediction

Modeling Dynamic Spatio-Temporal Correlations for Urban Traffic Flows Prediction

Prediction of traffic crowd movement is one of the most important component in many applications ' domains ranging from urban management to transportation schedule. The key challenge of citywide crowd flows prediction is how to model spatial and dynamic temporal correlation. However, in recent...

Full description

Bibliographic Details
Main Authors: Nabeela Awan, Ahmad Ali, Fazlullah Khan, Muhammad Zakarya, Ryan Alturki, Mahwish Kundi, Mohammad Dahman Alshehri, Muhammad Haleem
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Deep learning
urban crowd flows
neural networks
long short-term memory
convolutional neural network
Online Access:https://ieeexplore.ieee.org/document/9345698/

Internet

https://ieeexplore.ieee.org/document/9345698/

Similar Items