Method to Adjust ITE Vehicle Trip-Generation Estimates in...

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

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

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).

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

• 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

• 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

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.

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.

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?

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

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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)

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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?

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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AM(8 Sites)

PM(9 Sites)

AM(4 Sites)

PM(4 Sites)

AM(12 Sites)

PM(11 Sites)

Office Coffee Shop Residential

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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…

• 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

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)

Model Development: Dependent Variable

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veh tripsestimated ITE

veh tripsactualln

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.

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)

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)

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

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

Model Verification

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Observed versus Predicted Ratios to ITE Estimates:

20 Most Appropriate Portland Sites

Image Source: Andrew McFadden, UC Davis

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

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

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

Spreadsheet Tool

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Downtown LA Example: 72% vehicle trip reduction from ITE during PM peak

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

Questions & Discussion

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For more information, see the project website:

http://ultrans.its.ucdavis.edu/projects/ smart-growth-trip-generation

• 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.

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

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

Model Verification

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Observed versus Predicted Ratios to ITE Estimates:

All 78 Sites

Image Source: Andrew McFadden, UC Davis