Exploring the Influence of Urban Form on Travel and Energy Consumption, using Structural Equation Modeling
Exploring the Influence of Urban Form on Travel and Energy Consumption, using Structural Equation Modeling
Files
Publication or External Link
Date
2012
Authors
Liu, Chao
Advisor
Ducca, Frederick W
Citation
DRUM DOI
Abstract
This dissertation has contributed to the current knowledge by gaining additional insights into the linkages of different aspects of the built environments, travel behavior, and energy consumption using Structural Equation Modeling (SEM) that provides a powerful analytic framework for a better understanding of the complex relationships of urban form, travel and energy consumption. Several urban form measurements (density, mixed land use index, street network connectivity, regional accessibility, and distance to transit) were gathered from multiple external sources and utilized for both trip/tour origins and destinations. This dissertation also contributed to the analysis framework by aggregating trips into tours to test whether the tour-based analysis generates better results than the trip-based analysis in terms of model fit, significance, and coefficient estimations. In addition to that, tour-based samples were also stratified into three different classification schemes to investigate
the variations of relationship of urban form and travel among auto and transit modes and among various travel types.: (1) by modes (i.e. auto and transit); (2) by travel purposes (i.e. work, mixed, and non-work tours); and (3) by modes and purposes (first by modes, then by purpose). Stratification by purposes and modes provided an in-depth investigation of the linkages of urban form and travel behavior.
The research findings are many: (1) urban form does have direct effects on travel distance for all tour types modeled; (2) urban form at the destination ends has more influence than on the origin ends; (3) Urban form has indirect effects on travel distance and energy consumption through affecting driving patterns, mode choice, vehicle type and tour complexity; (4) People tend to drive when they have complicated travel patterns; (5) The effects of intermediate variables (driving patterns, tour complexity, mode choice, and vehicle type) are stronger than the direct effects generated from urban form; (6) Tour-based analyses have better model fit than trip-based analysis; (7) Different types and modes of travel have various working mechanisms for travel behavior. No single transportation technology or land use policy action can offer a complete checklist of achieving deep reductions of travel and energy consumption while preserving mobility of driving.