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This study seeks to investigate the variations associated with lane lateral locations and days of the week in the stochastic and dynamic transition of traffic regimes (DTTR). In the proposed analysis, hierarchical regression models fitted using Bayesian frameworks were used to calibrate the transition probabilities that describe the DTTR. Datasets of two sites on a freeway facility located in Jacksonville, Florida, were selected for the analysis. The traffic speed thresholds to define traffic regimes were estimated using the Gaussian mixture model (GMM). The GMM revealed that two and three regimes were adequate mixture components for estimating the traffic speed distributions for Site 1 and 2 datasets, respectively. The results of hierarchical regression models show that there is considerable evidence that there are heterogeneity characteristics in the DTTR associated with lateral lane locations. In particular, the …