Estimating the Vehicle-Miles-Traveled Implications of Alternative Metropolitan Growth Scenarios: A Boston Example
- Acknowledgements: We would like to thank MAPC, and especially Tim Reardon and Holly St. Clair in Data Services, for their comments and collaboration throughout this work. We also thank Christian Jacqz and Dan Marrier at MassGIS for their considerable effort in building the 250 × 250m grid cell layers for Massachusetts and in developing the annual mileage estimates and grid cell locations from the millions of vehicle records obtained from the Registry of Motor Vehicles. We also acknowledge those in MIT class 11.521 who worked on projects that contributed to this article: during 2008, Wanli Fang, Paul Green, Lissa Harris, Shan Jiang, Masayoshi Oka, Abner Oliveira, Yi Zhu, and Fabio Carrera; During 2009: Andrew Gulbrandson, Casey Hunter, Yang Jiang, Jae Seung Lee, Lulu Xue, and Jiyang Zhang; During 2010: Jie Xia. Finally, we acknowledge the helpful comments and suggestions from the reviewers and partial support from University Transportation Center Region One grant, MITR21–4, “New Data for Relating Land Use and Urban Form to Private Passenger Vehicle Miles,” and from the Singapore National Research Foundation through the “Future Urban Mobility” program of the Singapore/MIT Alliance for Research and Technology.
Address for correspondence: Joseph Ferreira Jr., Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA. E-mail: email@example.com
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 × 250 m 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.