Modeling the Effect of Land Use on Person Miles Traveled by Using Geographically Weighted Regression

This study contributes to the literature by developing a geographically weighted regression (GWR) model for capturing the effect of land use on person miles traveled (PMT) and demonstrating its benefits over simpler methods. Travel survey and land use data from southeast Florida were used in this analysis. The empirical results reconfirm the strong effects of regional accessibility, land use mixing, and connectivity on PMT. These land use effects were estimated to be significant after controlling for socioeconomic variables. Further, the GWR model demonstrated that the marginal sensitivities of PMT to various land use attributes vary over space. This spatial variation was particularly strong in the case of the effect of regional accessibility. Empirical results show that allowing for flexible trends in the parameter effects improves the models and explains a greater proportion of the variance in PMT across the region. The study also highlights the statistical superiority of the GWR model over the global regression models.