Estimation of VMT using heteroskedastic log-linear regression models

ABSTRACT

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.

KEYWORDS:

• VMT
• AADT
• log-linear regression
• heteroskedastic linear regression
• count data models

Acknowledgments

Data used for this project were obtained as a part of NCHRP project 17-81 - Proposed Macro-Level Safety Planning Analysis Chapter for the Second Edition of the Highway Safety Manual.

Disclosure statement

No potential conflict of interest was reported by the authors.

Disclaimer

The contents of this paper reflect the views of the authors who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Federal Highway Administration or the Commonwealth of Pennsylvania at the time of publication. This paper does not constitute a standard, specification, or regulation.