Method to Adjust ITE Vehicle Trip-Generation Estimates in...
Transcript of Method to Adjust ITE Vehicle Trip-Generation Estimates in...
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Method to Adjust ITE Vehicle Trip-Generation Estimates in Smart-Growth Areas
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Robert J. Schneider, Kevan Shafizadeh, & Susan L. Handy
University of Wisconsin-Milwaukee, CSU Sacramento, & UC Davis
TRB Innovations in Travel Modeling Conference—April 2014
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Overview
• Definitions
• Need for adjustments to ITE
• Other adjustment methods
• Development of adjustment model in CA
• Considerations & future research
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Image source: Benjamin Sperry
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Definitions
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• Smart-Growth (SG) Study Site: One of the 65 locations where data were collected for this study. Most were individual land uses; some MXDs.
• Trip: Movement between a person’s last activity location and the targeted use (inbound) or between the targeted land use and the next activity location (outbound).
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How many vehicle trips are generated by a specific land use?
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• The guidance used most often for estimating trip generation is the Institute of Transportation Engineers (ITE) Trip Generation Handbook.
• California Environmental Quality Act (CEQA),
requires developers in CA to estimate the transportation impacts of proposed developments.
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Need for Adjustments to ITE Trip Generation
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Need for Adjustments to ITE Trip Generation
• Research suggests that vehicle use is generally lower at smart growth developments…
Authors (Year) Study Locations General Findings
Arrington & Cervero (2008)
17 TOD Study Sites (Philadelphia, Portland, DC, & San Francisco regions)
• Weekday trips were 44% lower than ITE • AM peak trips were 49% lower than ITE • PM peak trips were 48% lower than ITE
Kimley Horn & Associates (2009)
16 Infill Study Sites (Los Angeles, San Diego, & San Francisco Regions)
3 mid-rise apartments: • AM peak trips were 27% lower than ITE • PM peak trips were 28% lower than ITE
4 general office buildings: • AM peak trips were 50% lower than ITE • PM peak trips were 50% lower than ITE
2 quality restaurants: • AM peak trips were 35% lower than ITE • PM peak trips were 26% lower than ITE
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• On average, ITE-estimates were 2.3 times higher than actual vehicle-trips in the AM peak hour
• On average, ITE-estimates were 2.4 times higher than actual vehicle-trips in the PM peak hour
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ITE-Estimated Vehicle-Trips vs. Actual Vehicle-Trips at 30 CA SG Sites
Schneider, R.J., K. Shafizadeh, B.R. Sperry, and S.L. Handy. “Methodology to Gather Multimodal Trip Generation Data in Smart-Growth Areas,” Transportation Research Record: Journal of the Transportation Research Board, Volume 2354, pp. 68-85, 2013.
Need for Adjustments to ITE Trip Generation
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• Using the ITE Trip Generation methodology on SG projects likely over-estimates vehicle trips Mitigation over-emphasizes vehicle needs and under-supplies transit, pedestrian, & bicycle facilities
• ITE Trip Generation rates remain widely used in practice and are based on large amount of data.
How can ITE Trip Generation Estimates be modified or adjusted for smart growth locations?
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Need for Adjustments to ITE Trip Generation
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Previous Methods to Adjust ITE Trip Generation Estimates
• ITE Multi-Use Method (ITE 2004) • NCHRP 8-51 Method (Bochner et al. 2011) • EPA/SANDAG Method (SANDAG 2010) • URBEMIS Method (Jones & Stokes Associates 2007) • MTC Survey Method (MTC 2006) • San Francisco Method (City and County of SF 2002) • New York City Method (Rizavi and Yeung 2010)
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Shafizadeh, K., R. Lee, D. Niemeier, T. Parker, and S. Handy. “Evaluation of Operation and Accuracy of Available Smart Growth Trip Generation Methodologies for Use in California.” Transportation Research Record: Journal of the Transportation Research Board, 2307:120-131, 2012.
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Previous Methods to Adjust ITE Trip Generation Estimates
• Practical limitations of all methods – (e.g., ease of use, sensitivity to SG variables, input
requirements, output features)
• All methods performed better than ITE, but no method was superior to others (based on 22 sites)
• SF & NYC methods were not applicable to other areas
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Shafizadeh, K., R. Lee, D. Niemeier, T. Parker, and S. Handy. “Evaluation of Operation and Accuracy of Available Smart Growth Trip Generation Methodologies for Use in California.” Transportation Research Record: Journal of the Transportation Research Board, 2307:120-131, 2012.
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Other Methods to Estimate Trip Generation
Recent US efforts: • Seattle, WA built environment categories—
probability of choosing auto (Clifton et al. 2012) • Portland, OR intercept surveys at 78 establishments—
linear regression model to adjust ITE (Clifton et al. 2012) • Household travel survey-based methods—
NCHRP Report 758 (Daisa et al. 2013); (Currans & Clifton 2014)
International methods: • UK Trip Rate Information Computer System (TRICS) • New Zealand Trips and Parking Database Bureau
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Would it work to apply a single adjustment factor to ITE estimates
all Smart Growth sites?
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Example Site 1: 343 Sansome, SF (Office)
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Example Site 2: Park Tower, Sacramento (Coffee)
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Example Site 3: Artisan on 2nd, LA (Residential)
15 Photo by Ben Sperry, Texas A&M Transportation Institute
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ITE Actual ITE Actual ITE Actual
343 Sansome, SF(Office)
Park Tower,Sacramento (Coffee)
Artisan on 2nd, LA(Residential)
PM
Pe
ak H
ou
r V
eh
icle
-Tri
ps
PM Peak Hour Vehicle-Trip Examples
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5.8 X
3.5 X 1.4 X
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ITE Actual ITE Actual ITE Actual
343 Sansome, SF(Office)
Park Tower,Sacramento (Coffee)
Artisan on 2nd, LA(Residential)
PM
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eh
icle
-Tri
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PM Peak Hour Vehicle-Trip Examples
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5.8 X
3.5 X 1.4 X
Study Motivation: What characteristics account
for differences in ITE overestimates within Smart Growth areas?
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Average Discrepancy by LU Category (CA Smart Growth Sites)
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0.0
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1.0
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2.0
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AM(8 Sites)
PM(9 Sites)
AM(4 Sites)
PM(4 Sites)
AM(12 Sites)
PM(11 Sites)
Office Coffee Shop Residential
Ave
rage
Dis
cre
pan
cy (
ITE
Ve
hic
le T
rip
s/A
ctu
al V
eh
icle
Tri
ps)
ITE
Ove
rest
imat
esIT
EU
nd
eres
tim
ates
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• ITE-estimates were 2.3 to 2.4 times higher than actual vehicle-trips (on average)
• Evidence of differences by land use category… – Office: ITE was 2.9 times higher in AM and 3.2 times higher in PM – Residential: ITE was 1.1 times higher in AM and 1.4 times higher in PM – Coffee: ITE was 2.6 times higher in AM and 1.2 times higher in PM
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Schneider, R.J., K. Shafizadeh, B.R. Sperry, and S.L. Handy. “Methodology to Gather Multimodal Trip Generation Data in Smart-Growth Areas,” Transportation Research Record: Journal of the Transportation Research Board, Forthcoming, 2013.
A single adjustment factor may not be appropriate for all Smart Growth sites…
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• ITE-estimates were 2.3 to 2.4 times higher than actual vehicle-trips (on average)
• Evidence of differences by land use category… – Office: ITE was 2.9 times higher in AM and 3.2 times higher in PM – Residential: ITE was 1.1 times higher in AM and 1.4 times higher in PM – Coffee: ITE was 2.6 times higher in AM and 1.2 times higher in PM
• Differences by Smart Growth characteristics?…
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Schneider, R.J., K. Shafizadeh, B.R. Sperry, and S.L. Handy. “Methodology to Gather Multimodal Trip Generation Data in Smart-Growth Areas,” Transportation Research Record: Journal of the Transportation Research Board, Forthcoming, 2013.
A single adjustment factor may not be appropriate for all Smart Growth sites…
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Development of California Smart-Growth Trip Generation Model
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Smart Growth Criteria • Mostly developed within 0.5 miles of site • Mix of land uses within 0.25 miles of site • Minimum jobs and population within 0.5
miles of site: J>4,000 and R>(6,900-0.1J) Land Use Classification Criteria • Mid- to High Density Residential
(ITE Codes 220, 222, 223, 230, 232) • Office (710) • Restaurant (931, 939) • Coffee/donut shop (936) • Retail (820, 867, 880) Transportation System Criteria • Minimum number of bus or transit lines • Bicycle facilities or sidewalk coverage
Criteria for Smart Growth Model Application
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Sites Used for Model Development
Sources: 1) EPA MXD Study (2010), 2) SANDAG MXD Study, (2010) 3) Caltrans Infill Study (2009), 4) TCRP Report 128 (2008), 5) Fehr & Peers (2010), 6) UC Davis Team field data collection (2012)
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AM Model PM Model
Residential Land Use 20 20
Office Land Use 11 12
Coffee/Donut Land Use 3 3
MXD Land Use 11 11
Retail Land Use 0 3
Other Land Use 1 1
Total Sites 46 50
(Los Angeles, San Diego, San Francisco, and Sacramento Regions)
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Model Development: Dependent Variable
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veh tripsestimated ITE
veh tripsactualln
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Explanatory Variables
• Land use classification (e.g., office, coffee/donut shop)
• Site characteristics (e.g., off-street surface parking, building setback)
• Adjacent street characteristics (e.g., number of lanes; pedestrian and bicycle facilities)
• Surrounding area characteristics (e.g., population & employment density, neighborhood socioeconomics)
• Proximity characteristics (e.g., distance to transit, distance to retail, distance to university campuses)
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First Tried One-Step Linear Regression Model
• Attempted to identify singular variables most strongly associated with reduced trips
• Challenge: many SG variables are highly correlated (e.g., high employment density, less off-street parking, metered on-street parking & more transit service)
• It is likely that many SG variables are working
together collectively to influence mode choice
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Decided on Two-Step Approach: Factor Analysis then Linear Regression Model
Factor Analysis • Identifies smart growth
variables that may be “working together”
• Quantifies the cumulative impact of this set of variables
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Factor Analysis: Smart Growth Factor
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Variable Coefficient*
Population within 0.5 miles (000s) 0.099
Jobs within 0.5 miles (000s) 0.324
Distance to center of CBD (in miles) -0.138
Average building setback from sidewalk -0.167
Metered parking within 0.1 miles (1=yes, 0 = no) 0.184
Number of bus lines within 0.25 miles 0.227
Number of rail lines within 0.5 miles 0.053
Percent of site area covered by surface parking -0.080
*This coefficient is applied to the standardized version of the variable which is calculated by subtracting the mean and dividing by the standard deviation from the 50 PM analysis sites.
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Linear Regression: Final AM and PM Peak Hour Models
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Dependent Variable = Natural Logarithm of Ratio of Actual Peak Hour Vehicle Trips to ITE-Estimated
Peak Hour Vehicle Trips
AM Model PM Model
Coeff. t-value p-value Coeff. t-value p-value
Smart Growth Factor -0.096 -0.857 0.397 -0.155 -1.491 0.143
Office land use (1 = yes, 0 = no) -0.728 -3.182 0.003 -0.529 -2.558 0.014
Coffee shop land use (1 = yes, 0 = no) -0.617 -1.677 0.101 -0.744 -2.339 0.024
Mixed-use development (1 = yes, 0 = no) -0.364 -1.561 0.127 -0.079 -0.381 0.705
Within 1 mi. of university (1 = yes, 0 = no) -1.002 -2.285 0.028 -0.311 -1.099 0.278
Constant -0.304 -2.460 0.018 -0.491 -4.469 0.000
Overall Model
Sample Size (N) 46 50
Adjusted R2-Value 0.294 0.290
F-Value (Test value) 4.74 (p = 0.002) 4.99 (p = 0.001)
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Linear Regression: Final AM and PM Peak Hour Models
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Dependent Variable = Natural Logarithm of Ratio of Actual Peak Hour Vehicle Trips to ITE-Estimated
Peak Hour Vehicle Trips
AM Model PM Model
Coeff. t-value p-value Coeff. t-value p-value
Smart Growth Factor -0.096 -0.857 0.397 -0.155 -1.491 0.143
Office land use (1 = yes, 0 = no) -0.728 -3.182 0.003 -0.529 -2.558 0.014
Coffee shop land use (1 = yes, 0 = no) -0.617 -1.677 0.101 -0.744 -2.339 0.024
Mixed-use development (1 = yes, 0 = no) -0.364 -1.561 0.127 -0.079 -0.381 0.705
Within 1 mi. of university (1 = yes, 0 = no) -1.002 -2.285 0.028 -0.311 -1.099 0.278
Constant -0.304 -2.460 0.018 -0.491 -4.469 0.000
Overall Model
Sample Size (N) 46 50
Adjusted R2-Value 0.294 0.290
F-Value (Test value) 4.74 (p = 0.002) 4.99 (p = 0.001)
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Linear Regression: Final AM and PM Peak Hour Models
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Dependent Variable = Natural Logarithm of Ratio of Actual Peak Hour Vehicle Trips to ITE-Estimated
Peak Hour Vehicle Trips
AM Model PM Model
Coeff. t-value p-value Coeff. t-value p-value
Smart Growth Factor -0.096 -0.857 0.397 -0.155 -1.491 0.143
Office land use (1 = yes, 0 = no) -0.728 -3.182 0.003 -0.529 -2.558 0.014
Coffee shop land use (1 = yes, 0 = no) -0.617 -1.677 0.101 -0.744 -2.339 0.024
Mixed-use development (1 = yes, 0 = no) -0.364 -1.561 0.127 -0.079 -0.381 0.705
Within 1 mi. of university (1 = yes, 0 = no) -1.002 -2.285 0.028 -0.311 -1.099 0.278
Constant -0.304 -2.460 0.018 -0.491 -4.469 0.000
Overall Model
Sample Size (N) 46 50
Adjusted R2-Value 0.294 0.290
F-Value (Test value) 4.74 (p = 0.002) 4.99 (p = 0.001)
Bold values indicate p-values < 0.15
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High & Low Examples (PM Model)
• Office project with highest value SGF in sample = 2.41 – Ratio actual/ITE-estimated is 0.248 – 75% vehicle trip reduction from ITE
• Office project with lowest value SGF in sample = -1.44 – Ratio actual/ITE-estimated is 0.451 – 55% vehicle trip reduction from ITE
• Residential project with lowest value SGF in sample = -1.44 – Ratio actual/ITE-estimated is 0.765 – 23% vehicle trip reduction from ITE
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PM Model Validation (N = 13)
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PM Model Validation (N = 13)
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Sneak Preview: Model Verification
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How well does the PM model work at a sample of sites in a
different urban region? Portland, OR
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Model Verification
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Observed versus Predicted Ratios to ITE Estimates:
20 Most Appropriate Portland Sites
Image Source: Andrew McFadden, UC Davis
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Model Verification
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ITE- and Model- Estimated Trips vs.
Actual Trips: 20 Most Appropriate
Portland Sites
Image Source: Andrew McFadden, UC Davis
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Modeling Considerations
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• Small sample size (N=46; N=50) • Considered variables for LU mix; residential LU • MXD sites (not used in model application) • Did not account for some variation
– e.g., Economic activity, attitudes
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Model Development: Big Picture
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• Final models balance theory and practice • Complement existing ITE Trip Generation method • Two-step method was a key breakthrough
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Spreadsheet Tool
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Downtown LA Example: 72% vehicle trip reduction from ITE during PM peak
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Future Research: Outstanding Transportation Impact Assessment Issues
• Should we use existing ITE Trip Generation Manual data (isolated, suburban site database) as a basis for SG adjustments?
• Model multimodal person trips • Measuring impact: number of trips vs. trip length
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Acknowledgements
• California Department of Transportation – Terry Parker, Project Manager – Practitioner Panel
• Data collection team members – Ewald & Wasserman Research Consultants – Gene Bregman & Associates – Manpower
• Data entry and Q/C team members – Calvin Thigpen, UC Davis – Mary Madison Campbell, UC Davis
• Data collection methodology – Brian Bochner, TTI – Ben Sperry, TTI
• Property managers and developers
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For more information, see project website: http://ultrans.its.ucdavis.edu/projects/smart-growth-trip-generation
Image source: Benjamin Sperry
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Questions & Discussion
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For more information, see the project website:
http://ultrans.its.ucdavis.edu/projects/ smart-growth-trip-generation
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• Based on data from 50 PM sites • Principal Axis Factoring (accommodates variables
that are not normally-distributed) • The single Smart Growth Factor (SGF) explained
49.5% of the variation in the data, while the second factor only explained 17.3% of the variation
• The ratio of the sample size and the number of variables included in the SGF is 50/8 = 6.25/1. This is similar to many studies reviewed in Costello and Osborne (2005).
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Factor Analysis: Smart Growth Factor
Useful Reference: Costello, A.B. and J.W. Osborne. “Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most from Your Analysis,” Practical Assessment, Research and Evaluation, 10(7). Available online: http://pareonline.net/getvn.asp?v=10&n=7, 2005.
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Factor Analysis: Smart Growth Factor Loadings
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Variable Loading
Population within 0.5 miles (000s) .538
Jobs within 0.5 miles (000s) .781
Distance to center of CBD (in miles) -.632
Average building setback from sidewalk -.636
Metered parking within 0.1 miles (1=yes, 0 = no) .707
Number of bus lines within 0.25 miles .745
Number of rail lines within 0.5 miles .661
Percent of site area covered by surface parking -.467
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San Francisco Region Study Sites
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Los Angeles Region Study Sites
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Sacramento Region Study Sites
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Future Research: Model Improvement
• More data to refine models; test in other regions • Need SG adjustments for more land uses
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78 Sites in Portland, OR
50 Data Source: Clifton, et al., Portland State University, 2012. Image Source: Andrew McFadden, UC Davis
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Model Verification
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Observed versus Predicted Ratios to ITE Estimates:
All 78 Sites
Image Source: Andrew McFadden, UC Davis