Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three m...
Main Authors: | Taghreed Alghamdi, Khalid Elgazzar, Taysseer Sharaf |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Future Internet |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-5903/13/9/225 |
Similar Items
-
Sensitivity Analysis on Hyperprior Distribution of the Variance Components of Hierarchical Bayesian Spatiotemporal Disease Mapping
by: I Gede Nyoman Mindra Jaya, et al.
Published: (2024-01-01) -
A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
by: Taghreed Alghamdi, et al.
Published: (2022-10-01) -
Online Bus Speed Prediction With Spatiotemporal Interaction: A Laplace Approximation-Based Bayesian Approach
by: Haipeng Cui, et al.
Published: (2021-01-01) -
A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology
by: Yufeng Wang, et al.
Published: (2024-03-01) -
Hierarchical Bayesian reliability assessment of energy networks
by: Tomas Iešmantas, et al.
Published: (2015-12-01)