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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 Bayesian models using the Gaussian process to predict long-term traffic status in urban settings. The Gaussian process is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. Performance evaluation on traffic data shows that the exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.