Please use this identifier to cite or link to this item: https://doi.org/10.1111/tgis.12017
Title: Estimating the vehicle-miles-traveled implications of alternative metropolitan growth scenarios: A Boston example
Authors: Ferreira, J.
Diao, M. 
Xu, J.
Issue Date: Oct-2013
Source: Ferreira, J., Diao, M., Xu, J. (2013-10). Estimating the vehicle-miles-traveled implications of alternative metropolitan growth scenarios: A Boston example. Transactions in GIS 17 (5) : 645-660. ScholarBank@NUS Repository. https://doi.org/10.1111/tgis.12017
Abstract: This study demonstrates the potential value, and difficulties, in utilizing large-scale, location aware, administrative data together with urban modeling to address current policy issues in a timely fashion. We take advantage of a unique dataset of millions of odometer readings from annual safety inspections of all private passenger vehicles in Metropolitan Boston to estimate the vehicle-miles-traveled (VMT) implication of alternative metropolitan growth scenarios: a sprawl-type "let-it-be" scenario and a smart-growth-type "winds-of-change" scenario. The data are georeferenced to 250 × 250m grid cells developed by MassGIS. We apply a greedy algorithm to assign Traffic Analysis Zone (TAZ) level household growth projections to grid cells and then use spatial interpolation tools to estimate VMT-per-vehicle surfaces for the region. If new growth households have similar VMT behavior as their neighbors, then the let-it-be scenario will generate 12-15% more VMT per household compared to the winds-of-change scenario. However, even the "wind-of-change" scenario, will result in new households averaging higher VMT per household than the Metro Boston average observed in 2005. The implication is that urban growth management can significantly reduce GHG but, by itself, will not be sufficient to achieve the GHG emission reduction targets set by the State for the transportation sector. © 2013 John Wiley & Sons Ltd.
Source Title: Transactions in GIS
URI: http://scholarbank.nus.edu.sg/handle/10635/113998
ISSN: 13611682
DOI: 10.1111/tgis.12017
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