Citation

Mahmud A, Hamilton I, Gayah VV, Porter RJ. Transp. Lett. 2024; 16(4): 320-329.

Copyright

(Copyright © 2024, Maney Publishing, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19427867.2023.2189802

PMID

unavailable

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.

Language: en

Keywords

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