Socioeconomic Determinants of Commercial Vehicle Miles Traveled: A Dynamic Regression Analysis

Öz

Vehicle Miles Traveled (VMT) is a key indicator used in transportation planning, infrastructure management, and environmental policy. Accurate forecasting of VMT is essential for understanding mobility demand and evaluating the economic factors that influence transportation activity. This study develops a dynamic regression framework to model annual commercial VMT in Los Angeles County over the period 2000–2020 using demographic and economic indicators. The model incorporates lagged dependent variables and lagged socioeconomic predictors to capture temporal persistence and delayed economic effects. Serial correlation in the disturbance term is addressed using generalized least squares (GLS) with an autoregressive AR(1) error structure. To ensure model parsimony given the relatively small annual sample size, model selection is conducted using the Bayesian Information Criterion (BIC). Three competing specifications are compared: a full ordinary least squares (OLS) model, a full GLS model with AR(1) errors, and a BIC-selected optimal GLS model. The results show that correcting for serial correlation substantially improves model fit and inference. Several contemporaneous and lagged socioeconomic variables are found to significantly influence VMT, indicating that travel demand responds to both current and prior economic conditions. Overall, the dynamic GLS framework provides an interpretable and effective approach for modeling and forecasting regional VMT, offering useful insights for transportation planning and policy analysis.

Anahtar Kelimeler

• : Vehicle Miles Traveled (VMT)
• dynamic regression
• generalized least squares (GLS)
• autoregressive errors
• transportation demand modeling
• time series forecasting

Kaynakça

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