Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond
10.1080/01441647.2023.2171151
Saved in:
Main Authors: | Haipeng Cui, Qiang Meng, Teng Teck-Hou, Xiaobo Yang |
---|---|
Other Authors: | CIVIL AND ENVIRONMENTAL ENGINEERING |
Format: | Article |
Published: |
Taylor & Francis
2023
|
Online Access: | https://scholarbank.nus.edu.sg/handle/10635/242100 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | National University of Singapore |
Similar Items
-
Machine learning in additive manufacturing : state-of-the-art and perspectives
by: Wang, Chengcheng, et al.
Published: (2020) -
Learning control: The state of the art and perspective
by: Xu, J.-X., et al.
Published: (2014) -
Forecasts in Corporate Reports: State of the Art
by: TAN, Teck Meng, et al.
Published: (1986) -
Computational study of state-of-the-art path-based traffic assignment algorithms
by: Chen, A., et al.
Published: (2014) -
Machine vision of traffic state estimation using fuzzy logic
by: Quiros, Ana Riza F., et al.
Published: (2017)