Open AccessMethod to adjust Institute of Transportation Engineers vehicle trip-generation estimates in smart-growth areas
Author(s) -
Robert J. Schneider,
Kevan Shafizadeh,
Susan Handy
Publication year - 2015
Publication title -
journal of transport and land use
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.05
H-Index - 27
ISSN - 1938-7849
DOI - 10.5198/jtlu.v0i0.416
Subject(s) - smart growth , trip generation , setback , context (archaeology) , transport engineering , regression analysis , sample (material) , population , variables , land use , engineering , computer science , statistics , geography , civil engineering , mathematics , chemistry , archaeology , demography , chromatography , trips architecture , sociology
JTLU vol. 8, no. 1, pp 69-83 (2015)This paper describes a practical method of adjusting existing Institute of Transportation Engineers (ITE) estimates to produce more accurate estimates of motor-vehicle trip-generation at developments in smart-growth areas. Two linear regression equations, one for an A.M. peak-hour adjustment and one for a P.M. peak-hour adjustment, were developed using vehicle trip counts and easily measured site and surrounding area context variables from a sample of 50 smart-growth sites in California. Many of the contextual variables that were associated with lower vehicle trip generation at the smart-growth study sites were correlated. Therefore, variables representing characteristics such as residential population density, employment density, transit service, metered on-street parking, and building setback distance from the sidewalk were combined into a single “smart-growth factor” that was used in the linear regression equations. The A.M. peak-hour and P.M. peak-hour adjustment equations are only appropriate for planning-level analysis at sites in smart-growth areas. In addition, the method is only appropriate for single land uses in several common categories, such as office, mid- to high-density residential, restaurant, and coffee/donut shop. The method uses data from California, but the methodological approach could provide a framework for adjusting ITE trip-generation estimates in smart-growth areas throughout the United States