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Vehicle miles traveled (VMT) is an essential input for many aspects of transportation engineering, and an accurate estimation of VMT is critical for practicing engineers. Linear regression models are a popular method to estimate VMT as they provide insight into the relationships between VMT and other external factors. In linear regression models the prediction of the response variable has a non-zero probability of resulting in a negative value. For this reason, the natural logarithm of VMT is often used as the response variable to force a positive outcome. However, these log-linear regression (LLR) models provide median VMT estimate instead of the mean estimate. To overcome this limitation of LLR models, this study proposes using heteroskedastic LLR and count data methods to estimate VMT. These methods are found to have better performance than LLR models in terms of data fit and prediction accuracy. [ABSTRACT FROM AUTHOR]

Traffic crashes vary in the manner in which the collision occurs (collision type), and countermeasures to reduce crashes might vary significantly based on this collision type. The inherent complexity in their mechanism has motivated this study to identify significant factors influencing collision types, with the goal of better countermeasure deployment. The objective of this work is to compare the performances of statistical and machine learning (ML) models in classifying crashes based on collision type, and assess their generalizability and interpretability. Discrete choice models, Bayesian classifiers, tree-based algorithms, and support vector machines are among the data-driven methods considered for comparison. Results indicate that tree-based algorithms perform consistently well and offer a higher interpretability, with out-of-distribution robustness. However, while ML models provide a flexible framework for modeling large data volumes, statistical models provide additional interpretability on the effect of critical variables on crash mechanisms – which is relevant from a safety management standpoint. [ABSTRACT FROM AUTHOR]