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.

[1]  A S Fotheringham,et al.  Geographically weighted Poisson regression for disease association mapping , 2005, Statistics in medicine.

[2]  Reid Ewing,et al.  Travel and the Built Environment , 2010 .

[3]  M. Cropper,et al.  The Effects of Urban Spatial Structure on Travel Demand in the United States , 2005, Review of Economics and Statistics.

[4]  Keith Ord,et al.  Spatial Autocorrelation: A Review of Existing and New Measures with Applications , 1970 .

[5]  Yiyi Wang,et al.  Anticipation of Land use Change through use of Geographically Weighted Regression Models for Discrete Response , 2011 .

[6]  L. Anselin.  Spatial Econometrics: Methods and Models , 1988 .

[7]  F. Zhao,et al.  Using Geographically Weighted Regression Models to Estimate Annual Average Daily Traffic , 2004 .

[8]  H. Fang,et al.  A discrete–continuous model of households’ vehicle choice and usage, with an application to the effects of residential density , 2008 .

[9]  Petter Næss.  Residential location affects travel behavior: but how and why? the case of Copenhagen metropolitan area , 2005 .

[10]  David Brownstone,et al.  SPECIAL REPORT 298: DRIVING AND THE BUILT ENVIRONMENT: THE EFFECTS OF COMPACT DEVELOPMENT ON MOTORIZED TRAVEL, ENERGY USE, AND CO2 EMISSIONS Key Relationships Between the Built Environment and VMT , 2008 .

[11]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[12]  K. Krizek.  Residential Relocation and Changes in Urban Travel: Does Neighborhood-Scale Urban Form Matter? , 2003 .

[13]  D. Brownstone,et al.  The Impact of Residential Density on Vehicle Usage and Energy Consumption , 2005 .

[14]  G. Giuliano,et al.  Another Look at Travel Patterns and Urban Form: The US and Great Britain , 2003 .

[15]  Yee Leung,et al.  Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model , 2000 .

[16]  Yingling Fan,et al.  The built environment, activity space and time allocation : an activity-based framework for modeling the land use and travel connection , 2007 .

[17]  Susan L Handy,et al.  Regional Versus Local Accessibility: Implications for Nonwork Travel , 1993 .

[18]  Reid Ewing,et al.  Travel and the Built Environment: A Synthesis , 2001 .

[19]  J. Malczewski,et al.  Residential Burglaries and Neighborhood Socioeconomic Context in London, Ontario: Global and Local Regression Analysis* , 2005 .

[20]  Susan L Handy,et al.  Smart Growth and the Transportation-Land Use Connection: What Does the Research Tell Us? , 2005 .

[21]  M. Cropper,et al.  The Impact of Urban Spatial Structure on Travel Demand in the United States , 2003 .

[22]  Clifford M. Hurvich,et al.  Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion , 1998 .

[23]  A. Shalaby,et al.  Development of planning level transportation safety tools using Geographically Weighted Poisson Regression. , 2010, Accident; analysis and prevention.

[24]  Huiquan Bi,et al.  Modeling spatial variation in tree diameter–height relationships , 2004 .

[25]  Keith Ord,et al.  Testing for Spatial Autocorrelation Among Regression Residuals , 2010 .

[26]  Kamar Ali,et al.  Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making? , 2007 .

[27]  J. Dargay,et al.  The impact of land use patterns on travel behaviour , 2003 .