Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling

Taghreed Alghamdi, Khalid Elgazzar and Taysseer Sharaf
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Taghreed Alghamdi: Faculty of Science, Ontario Tech University, Oshawa, ON L1G 0C5, Canada
Khalid Elgazzar: Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON L1G 0C5, Canada
Taysseer Sharaf: Data Scientist, Canton, MI 4818, USA

Future Internet, 2021, vol. 13, issue 9, 1-18

Abstract: 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 models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.

Keywords: hierarchical; Bayesian; spatiotemporal (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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