{
3.4 Verification of Methodologies: Process and Data
In order to test the application of each of these methodologies for adjustment, data were collected from land uses around the United States for a different land uses, during a range of time periods and throughout a variety of urban contexts. Vehicle trip ends were estimated using ITE’s Trip Generation Handbook methodology, and then adjusted (see Section 3.0) using on the nine land use type adjustment methods estimated and described previously:
Adjustment A: Single mode share table by a single built environment metric;
Adjustment B: Regression using the built environment metric with the best model performance; and
Adjustment C: Regression using a built environment metric with a strong model performance that is sensitive to land use policy.
Site-level data are limited. Because of this, a full validation across all land uses, time periods and days of the week was not possible. This section includes the results and
discussion of the methodology verification, which tests the applicability of this methodology to adjust site-level data collections for urban context. Further data collection to verify beyond the scope of what was tested in this section is necessary to determine the usefulness of HTS mode share and vehicle occupancy adjustment applications for those cases.
To compare the estimation methods, the normalized root-mean-squared error (NRMSE) was calculated for each land use category, data set and adjustment method. The NRMSE metric, shown in Equation 6, is an approximation of the standard deviation of the error of the estimate normalized across the range of observed vehicle trip end22 values. This measure is expressed as a percent. When a limited range in vehicle trip end counts are observed, which happens when the sample size is small and the establishments are similar in size within a category, the NRMSE may be inflated since the variation of the error is measured relative to the range of observed values. Generally, smaller percentages are preferred which indicate a rate of error that is small respective to the range of vehicle trip end counts.
Equation 6. Normalized Root Mean Squared Error (NRMSE)
√
∑
Where,
22 Vehicle trip rates were not used as a comparison due to the variation ITE’s Trip Generation Handbook methods used for estimation (e.g. weighted average rates or equations) and independent variable predictors (e.g. dwelling units, gross floor area or seats for restaurants).
year 2000 were evaluated. Data were provided by three sources: Dr. Kelly J. Clifton at Portland State University from a 2011 study (Clifton, Currans, & Muhs, 2012), a California-based data collection prepared by Kimley-Horn and Associates (Daisa, et al., 2009), and more recent data collections provided by the Institute of Transportation Engineers (ITE)23. The majority of the verification sample was collected in California and Oregon, but a small portion of the sample was located in Maryland and Vermont (Table 8).
Table 8. Distribution of Establishment-Level Data for Verification of Methodology
Metropolitan Organization (MPO) City State Sample
Percent
Metropolitan Transportation Commission Oakland California 15%
San Diego Association of Governments San Diego California 4%
Southern California Association of Governments Los Angeles California 5%
Metropolitan Washington Council of Governments near Washington D.C. Maryland 1%
Portland Area Comprehensive Transportation System Portland Oregon 71%
Chittenden County MPO South Burlington Vermont 1%
Non-MPO --- Vermont 4%
23 The 8th edition of the ITE’s Trip Generation Handbook was applied to estimate vehicle trip ends, therefore, data that were included within the 8th edition were not included within this analysis.
Additionally, data were collected at a variety of land uses during a range of time periods.
Table 9 details the distribution of the sample used to analyze the application of the adjustment methodology to verify the use at a range of land uses across typical time periods defined by ITE’s Trip Generation Handbook. The majority of the sample tested is for the PM Peak Hour of the adjacent street traffic.
Table 9. Time of Data Collection for Establishment-Level Data for Verification Methodology ITE's Trip Generation
3.4.2 Built Environment Measures
To apply any of the adjustment methods described previously, six measures defining the built environment, regional accessibility, and transit accessibility must be calculated.
Only some of the built environment measures need to be calculated to apply any one of the three adjustment methods selected for each land use. The built environment measures utilized for each land use are listed in the adjustment specific models and tables in APPENDIX F through APPENDIX I. The sources for the required built environment measures are shown in Table 10 and Table 11.
Table 10. Built Environment Measures for Application: Data Sources
Data Source Year Files
Census Summary File 1 2010 Table P1: Total Population Longitudinal Employer-Household
Dynamics (LEHD) Origin-Destination Employment Statistics
2008 Workplace Area Characteristics (WAC), All Jobs24
TIGER Files 2009 Edges and Faces
Transit-Oriented Development (TOD)
Database 2012 Point locations of TODs within each region
24 LEHD data are not reported for New Hampshire.
Table 11. Built Environment Measures for Application: Definitions25 26
Measure Units Source
Distance of Destination to CBD27 Miles
Presence of TOD28 Binary TOD Database
Residential
Population Density Residents per acre Census 2010 SF1, P1
Employment
Employment Density Employees per acre LEHD
Activity
Activity Density (Population + Employment) Employees and
residents per acre LEHD
Connectivity
Total Intersection Density Intersections per acre TIGER Four Approach (or more) Intersection Density Intersections per acre TIGER
25 All items, unless otherwise noted, were calculated using GIS protocols set forth by D’sousa et al (2012).
26Area calculations for each AOI used to calculate densities include water area.
27 Distance of Destination to Central Business District (CBD) is the Euclidian distance (miles) from the destination trip end of each trip to the CBD for the given region.
28 Presence of TOD is a binary measure indicating the presences of a TOD within the AOI.