Reducing vehicle miles traveled through smart land-use design.

The objective of the present study was to help planning and transportation organizations across New York State identify the most appropriate methods that can evaluate the likely impacts of smart growth strategies. Two approaches were investigated using the Greater Buffalo/Niagara metropolitan area as a case study. The first approach involved a GIS-based methodology by which spatial characteristics of the built environment were quantified and used to predict travel behavior at an aggregate level. A wide scope of travel behavior was examined, and over 50 variables, many of which are based on high-detail data sources, were investigated for potentially quantifying the built environment. Linear modeling was then used to relate travel behavior and the built environment, yielding models that may be applied in a post-processor fashion to travel models’ results to provide some measure of sensitivity to built environment modifications. The second approach developed an enhanced travel demand forecasting method to evaluate the impact of smart growth strategies on travel patterns. Though the modelling framework shares a similar structure as the traditional four-step planning method, behavior choice models were developed in order to capture the impact of land use on travel behavior. The enhanced travel demand forecasting method was tested using the Greater Buffalo-Niagara Area as the study case. Findings support the claims that compact, mixed-use, pedestrian-friendly and transit-friendly designs can reduce vehicle trips, encourage non-motorized modes, decrease average trip length, and reduce daily VMT. Moreover, the study has developed two useful methodologies which can be applied to increase the sensitivity of current modeling tools toward assessing the likely impacts of proposed smart growth strategies.