advances in estimating the transportation impact of development for urban locations

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Advances in Estimating the Transportation Impact of Development for Urban Locations

An IBPI Webinar

Kristina M. CurransCivil and Environmental Engineering, Portland State University

Adviser: Dr. Kelly J. Clifton

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Outline

• Background• Methods of Adjustments for Urban

Locations– Urban Context Adjustment (UCA)– Smart-Growth Trip Generation (SGTG)

Adjustment– Household Travel Survey (HTS)

Adjustment

• Conclusions

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Current state-of-the-practice for estimating trip generation for Traffic Impact Analysis

Includes:• Methodology• ~160 land uses• ~550 locations• ~5,000 points

ITE’s Trip Generation Handbook1

1(ITE, 2004; ITE, 2012)

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Dependent predictors are only establishment size

Vehicle trips only

By time of day, day of week

Biased toward suburban, automobile-oriented locations

Not sensitive to urban contexts

ITE’s Trip Generation Handbook

*Graphic from (ITE, 2008)

*

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1(Bochner et al, 2011; Clifton et al, 2013; Daisa et al, 2009; Schneider et al, 2013)2(Cervero et al, 1997; Ewing et al, 2001; Ewing et al, 2010)

Establishing the Need

• Studies show ITE lacks sensitivity to urban context1

• Growing literature establishing the relationship between land use, the built environment and travel behavior2

• Has not yet been incorporated into ITE applications

• Several methods have been published

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Terminology

• Primary Data (trip counts, mode splits, etc)– Establishment or intercept surveys– ITE vehicle trip generation data

• Secondary Data (modal behavior)– Household travel surveys

• Supplementary Data (environmental)– Census, ACS– Built environment or land use

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Types of Trip Generation Estimation Methods for Urban Contexts

1. Estimate multimodal rates based on primary data

2. Estimate vehicle rates based on primary data (e.g. ITE), adjust for multimodal travel using primary data– Urban Context Adjustment, Smart-Growth Trip

Generation Adjustment

3. Estimate vehicle rates based on primary data (e.g. ITE), adjust for multimodal travel using secondary data– Household Travel Survey Adjustment

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URBAN CONTEXT ADJUSTMENT (UCA) MODEL

Portland State University, OTREC

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Urban Context Adjustment (UCA) Models(Clifton et al, 2014)

• Urban vehicle trip rate adjustment to ITE

• Developed with primary data (N = 78) – Portland, Oregon– High-turnover (sit-down) restaurants,

(24-hour) convenience markets, drinking places

• Tested with primary data (N = 34)

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Dependent Variables:

Independent Variables:

Note: Drinking places are the base case for the model

Urban Context Adjustment (UCA) Models

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Built Environment () (=1)

(=1)

1. Number of Transit Corridors -0.1 -25.5 7.6 -4.3

2. People Density -0.1 -26.2 7.2 -3.4

3. Number of High-Frequency Bus Routes -0.1 -26.1 7.2 -3.6

4. Employment Density -0.1 -26.1 7.2 -4.2

5. Lot Coverage -0.2 -26.6 7.0 -0.9

6. Length of Bike Facilities -0.8 -26.2 7.6 -0.8

7. Rail Access -4.0 -24.3 8.1 -5.2

8. Intersection Density -0.6 -26.8 6.7 -0.9

9. Median Block Perimeter 1.3 -26.2 6.9 -8.6

10. Urban Living Infrastructure (ULI) -3.3 -26.0 7.4 0.6

Bold: significantly different from zero at a 95% confidence level; adjusted R2 from 0.75 to 0.77

UCA Models (by BE measure)

ULI in Metro Region

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Example Application• Convenience market• Location has an ULI of 2.9• Compute adjustment to ITE rate:

• Adjust ITE for context:New Adjusted Rate = ITE rate + ADJNew Adjusted Rate = 52.4 + (– 34.9) = 17.5 trip ends/1000 SQFT

= + + +

= + + +

= + + + =

= + + +

= + + +

= + + +

~[ -66% of ITE’s rate ]

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Testing

• Test adjustment method using new locations

• Data collected at 34 establishments • Vehicle counts, PM Peak, April-May

2012

Convenience Market (Open 24-

hours)Drinking Place

High-Turnover (Sit-Down)

RestaurantsLU (851) LU (925) LU (932)

Sample Size 10 12 12

Average Percent Error

UCA 32% 31% 68%

ITE 195% 119% 63%

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Benefits and Limitations

Benefits– Uses recently-collected, person-trip,

primary data– Simple, parsimonious method– Several models to choose from, based on

data available to the user

Limitations – Limited sample size, pooled models– Limited number of land uses, time periods

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SMART-GROWTH TRIP GENERATION (SGTG) MODEL

University of California, Davis for Caltrans

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Smart-Growth Trip Generation (SGTG) Model (Handy et al, 2013)

• Urban vehicle trips adjustment to ITE• Developed with primary data– Los Angeles, San Francisco, Oakland,

Sacramento

• Tested with primary data– Portland, Oregon

• AM (N = 46) & PM (N = 50) peak hour models

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Smart-Growth Trip Generation (SGTG) Model

• Land uses (N = 5)– Mid- to high-density residential, office,

restaurant, coffee/donut shop, retail

• Criteria for smart-growth locations (N = 68)– Mostly developed within 0.5 miles of site– Mix of land uses within ¼ mile– Minimum jobs (>4k) and population (>6900-

0.1*J) within ½ miles of site – Min. number of bus/transit lines– Bicycle facilities or sidewalk coverage

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Example Application

Download the Excel toolkit here: http://ultrans.its.ucdavis.edu/projects/smart-growth-trip-generation

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



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Example Application

Inputs:• ITE estimated vehicle

trips• Built environment

– Residential or emp. density

• Site characteristics – Off-street surface

parking, building setback

• Adjacent street characteristics – #lanes, bike/ped

facilities

• Proximity characteristics – Transit, retail, campuses





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Example Application

Outputs:• Smart growth factor

– Composite index of all built environment measure

• Estimated AM/PM peak hour vehicle trips adjusted for urban context





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Testing1

• Tested against PSU’s 78 sites (restaurant, convenience markets, drinking places)– 75% of sites were predicted closer to the

actual trip counts than to ITE’s estimate– Locations meeting the smart growth

criteria benefited the most

• Re-estimated model with CA/OR combined data

1(Handy et al, 2013)

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Benefits – Uses recently collected, primary, multimodal

data– Two time periods (AM/PM)– Additional land uses

Limitations– Limited sample size– More land uses and variables and smaller

sample size means less statistical power (significance) in tests

– Limited number of land uses

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HOUSEHOLD TRAVEL SURVEY (HTS) ADJUSTMENTS

Civil and Environmental Engineering, Portland State University

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Household Travel Survey (HTS) Adjustment

(Currans, 2013)

• Urban multimodal/vehicle trip adjustment to ITE

• Developed with secondary data – Oregon Household Activity Survey

(2011)– Puget Sound Regional Council (2006)– Baltimore NHTS Add-on (2001)

• Tested with primary data (N = 195)• Multiple models for land uses

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Primary Data

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Household Travel Surveys (HTS)

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Household Travel Surveys (HTS)

• Organized similar to ITE’s Trip Generation Handbook to support compatibility– Considered trip ends

• Entering and exiting traffic

– Classified by: • time of day (AM Peak, Midday, PM Peak)• day of week (Weekday, Friday, Weekend)• travel during winter months

• Controlled for:– Built environment– Distance to the Central Business District– Transit-Oriented Development

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Resulting Adjustments

• Simple mode share table

ACTIVITY DENSITY% M

OD

E S

HA

RE

INTERSECTION DENSITY% A

UTO

SH

AR

EPOPULATION DENSITY%

AU

TO

SH

AR

E

• Regressions with different built environment measures, control for addition travel information

Example Application

(1) Estimate Vehicle Trips using ITE’s Trip Generation Handbook

• 2,500 square feet of gross floor area

• PM Peak Hour of Adjacent Street Traffic (between 4-6PM)

ITE’s rate is 52.41 vehicle trip ends per 1,000 square feet of gross floor area.

2.5 * 52.41 = 131 Vehicle trip ends

Equation Development - 31

2,500sqft

131Vehicle

Trip Ends

*Graphic from (ITE, 2008)

*

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Example Application

(2) Convert ITE’s vehicle trips into person trips

(131 h𝑣𝑒 𝑖𝑐𝑙𝑒𝑡𝑟𝑖𝑝𝑠∗1.0𝑝𝑒𝑟𝑠𝑜𝑛𝑠𝑝𝑒𝑟 h𝑣𝑒 𝑖𝑐𝑙𝑒 )100% 𝑎𝑢𝑡𝑜𝑚𝑜𝑏𝑖𝑙𝑒𝑚𝑜𝑑𝑒 h𝑠 𝑎𝑟𝑒

=131 𝐼𝑇𝐸𝑝𝑒𝑟𝑠𝑜𝑛𝑡𝑟𝑖𝑝𝑠

( h𝑉𝑒 𝑖𝑐𝑙𝑒𝑇𝑟𝑖𝑝𝑠 𝐼𝑇𝐸∗ h𝑉𝑒 𝑖𝑐𝑙𝑒𝑂𝑐𝑐𝑢𝑝𝑎𝑛𝑐𝑦 𝐼𝑇𝐸)𝐴𝑢𝑡𝑜𝑚𝑜𝑏𝑙𝑒𝑀𝑜𝑑𝑒 h𝑆 𝑎𝑟𝑒 𝐼𝑇𝐸

=𝑃𝑒𝑟𝑠𝑜𝑛𝑇𝑟𝑖𝑝𝑠 𝐼𝑇𝐸

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Example Application

(3) Re-distribute ITE’s person trip estimates to urban-context modes• Classified as a “retail” location• PM Peak period• Population density: 7.3 residents per acre• Activity density: 14.6 people per acre • Within ½ mile of a Transit-Oriented Development: No • Distance to the Central Business District: 8.8 miles

𝑃𝑒𝑟𝑠𝑜𝑛𝑇𝑟𝑖𝑝𝑠 𝐼𝑇𝐸∗ 𝐴𝑢𝑡𝑜𝑚𝑜𝑏𝑖𝑙𝑒𝑀𝑜𝑑𝑒 h𝑆 𝑎𝑟𝑒𝑈𝑟𝑏𝑎𝑛𝐶𝑜𝑛𝑡𝑒𝑥𝑡

h𝑉𝑒 𝑖𝑐𝑙𝑒𝑂𝑐𝑐𝑢𝑝𝑎𝑛𝑐𝑦𝑈𝑟𝑏𝑎𝑛𝐶𝑜𝑛𝑡𝑒𝑥𝑡

= h𝑉𝑒 𝑖𝑐𝑙𝑒𝑇𝑟𝑖𝑝𝑠𝑈𝑟𝑏𝑎𝑛𝐶𝑜𝑛𝑡𝑒𝑥𝑡

131 𝐼𝑇𝐸𝑝𝑒𝑟𝑠𝑜𝑛𝑡𝑟𝑖𝑝𝑠∗91% automobile mode share 1.16𝑝𝑒𝑟𝑠𝑜𝑛𝑠𝑝𝑒𝑟 h𝑣𝑒 𝑖𝑐𝑙𝑒

=103 h𝑣𝑒 𝑖𝑐𝑙𝑒𝑡𝑟𝑖𝑝𝑠

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Application and Testing

• Independently-collected primary data– 195 points– 13 different types of establishments– Portland, Oregon; San Diego, Oakland,

LA, California; Washington, D.C area; Vermont

– OTREC1, Caltrans/Kimley-Horn2 and ITE

1(Clifton et al, 2013), 2(Daisa et al, 2009)

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ITE’s Handbook

• Residential condominiums/ townhouses

• Supermarkets

• Quality (sit-down) restaurants

Urban Adjusted

• High-rise apartments

• High-rise residential condominiums/ townhouses

• Convenience markets

• Shopping centers

• Coffee/donut shops

• Bread/donut/bagel shops

• Drinking places

• Office buildings

Similar Results

• Mid-rise apartments

• High-turnover (sit-down) restaurants

ITE’s Handbook

Similar Results

HTS Adjusted

Testing

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Benefits– Developed from data from multiple

regions– Estimates multimodal model splits– Applied to many land uses – Tested with 195 primary data points

Limitations– Adjustment not based on trip counts– Built from secondary data– Tested for 13 land uses, limited same size

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CONCLUSIONS

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Conclusions

• Limitations for all methods– Lack of multimodal, person trip, primary

data

• Adjustment methods are stop-gap alternatives that account for urban context, not direct estimation methods

• Call for data that represents people, not just cars

• Consistent framework for person trip, multimodal data collection1

1(Clifton et al, 2013; Schneider et al, 2013)

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Questions?

Kristina M. Currans Civil EngineeringPortland State [email protected]

mailto:[email protected]

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BIBLIOGRAPHY

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BibliographyARUP (2012). Trip Genie: Context-sensitive trip generation rates. Available online at: tripgenie.org. Baltimore Regional Transportation Board (2001). National Houeshold Travel Survey (NHTS) Baltimore Add-on.

Baltimore, Maryland.Cervero, R., & Kockelman, K. (1997). Travel Demand and the 3Ds: Density, Diversity, and Design.

Transportation Research: D, 2(3), 199-219.Clifton, K. J., Currans, K. M., & Muhs, C. D. (2012). Contextual Influences on Trip Generation, OTREC-RR-12-

13. Portland, Oregon: Oregon Transportation Research and Education Consortium (OTREC).Clifton, K. J., Currans, K. M., & Muhs, C. D. (2013). Evolving the Institute of Transportation Engineers' Trip

Generation Handbook: A Proposal for Collecting Multi-modal, Multi-context, Establishment-level Data," Transportation Research Record: Journal of the Transportation Research Board, Vols. No. 2344 Travel Demand Forecasting, Vol. 2, pp. 107-117.

Clifton, K. J., Currans, K. M., & Muhs, C. D. (2014). Adjusting ITE’s Trip Generation Handbook for Urban Context. Journal of Transport and Land Use [forthcoming].

Currans, K. M. Improving Vehicle Trip Generation Estimations for Urban Contexts: A Method Using Household Travel Surveys to Adjust ITE Trip Generation Rates. Dissertations and Theses. Paper 987, September, 2013. Available online at: http://pdxscholar.library.pdx.edu/open_access_etds/987/.

Daisa, J. M., Mustafa, A., Mizuta, M., Schwartz, L., Espelet, L., Turlik, D.,Bregman, G. (2009). Trip Generation Rates for Urban Infill Land Uses in California: Phase II Final Report. Kimley-Horn & Associates, Inc. California: California Department of Transportation (Caltrans).

Ewing, R., & Cervero, R. (2001). Travel and the Built Environment: A Synthesis. Transportation Research Record: Journal of the Transportation Research Board, 1780, pp. 87-114.

Ewing, R., & Cervero, R. (2010). Travel and the Built Environment: A Meta-Analysis. Journal of the American Planning Association, 76(3), pp. 265-294.

Handy, S. L.; Shafizadeh, K.; Schneider, R. J. (2013). California Smart-Growth Trip Generation Rates Study. University of California, Davis for the California Department of Transportation, Davis, California. Available online at: http://ultrans.its.ucdavis.edu/projects/smart-growth-trip-generation.


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BibliographyInstitute of Transportation Engineers (2004). Trip Generation Handbook, 2nd Edition: An ITE Recommended Practice.

Washington, D.C.: Institute of Transportation Engineers. Institute of Transportation Engineers (2012). Trip Generation 9th Edition: An Information Report. Washington, D.C.:

Institute of Transportation Engineers.Institute of Transportation Engineers (2008). Trip Generation 8th Edition: An Information Report. Washington, D.C.:

Institute of Transportation Engineers.New South Wales Roads and Traffic Authority (2002). Guide to Traffic Generation Developments, Version 2.2. Roads

and Traffic Authority (RTA), Sydney, Australia.New York City (2010). City Environmental Quality Review (CEQR): Chapter 16. New York City, NY: Mayor's Office of

Environmental Coordination.New Zealand Trips and Parking Database Bureau (NZTPDB) (2012). New Zealand Trips and Parking Database Bureau.

Available online at: www.tdbonline.org.Oregon Modeling Steering Committee (2009-2011). Oregon Household Activity Survey. Portland, Oregon. Available at:

http://www.oregon.gov/ODOT/TD/TP/pages/travelsurvey.aspxPuget Sound Regional Council (PSRC) (2006). Puget Sound Regional Travel Survey. Seattle, Washington.San Diego Association of Governments (SANDAG) (2010). Trip Generation for Smart Growth: Planning Tools for the San

Diego Region. San Diego, CA.San Francisco Planning Department (2002). Transportation Impact Analysis Guidelines for Environmental Review. San

Francisco, California: City and County of San Francisco.Schneider, R. J.; Shafizadeh, K.; Sperry, B. R.; Handy, S. L. (2013). Methodology to Gather Multimodal Trip Generation

Data in Smart-Growth Areas. Transportation Research Record: Journal of the Transportation Reserach Board, vol. 2354, pp. 68-85.

Trip Rate Information Computer System (TRICS) (2012). Trip Rate Information Computer System Good Practice Guide. UK and Ireland.

Virginia Department of Transportation (2008). LandTrack: Transportation Impact of Land Development. Available online at: http://landtrx.vdot.virginia.gov/.

Maps background by Stamen: http://maps.stamen.com

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BONUS SLIDES

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Alternative Data Sources

• National Primary Data– Trip Genie from ARUP (2012)

• Focuses on urban trip generation rates• Based on published data and reports• Limited multimodal data

• Local Primary Data– New York City (2010)– “LandTrack” from Virginia DOT (2008)– Outside the US

• Trip Rate Information Computer System or TRICS (2012) – UK and Ireland• New South Wales Road and Traffic Authority (2002) – Australia• New Zealand Trips and Parking Database Bureau or NZTPDB (2012)

• Local Adjustments to ITE, based on Primary Data– San Francisco Planning Department (2002)

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Establishing a Need for Local Rates2

ITE Criteria1

LU 851: Convenience Market (Open

24-Hours) (N=26)

LU 925: Drinking Place (N=13)

LU 932: High-Turnover (Sit-

Down) Restaurant (N=39)

A trip generation study (with at least three locations) provides a vehicle trip rate that falls within 1 standard deviation of the mean provided by ITE.

TGSRATE = 20.8ITERATE ± SD.= 31.0 -

73.8

TGSRATE = 4.9ITERATE ± SD.= 3.3 -

19.4

TGSRATE = 12.3ITERATE ± SD.= 2.0 - 20.3

At least 1 study site that falls above the ITE weighted average or equation, and 1 that falls below;

ORAll study locations fall within 15% of the ITE average rate or equation.

0 locations fall above, 26 location

fall below

OR

1 of 26 location falls within 15%

0 locations fall above, 13 locations

fall below

OR

0 of 13 locations fall within 15%

17 locations fall above, 22 locations

fall below

OR

7 of 39 locations fall within 15%

Locally collected studies fall within the scatter of rates provided by ITE

Appear slightly below Appear below Appear within scatter

"Common sense" indicates appropriate use of ITE rates for location application.

Vague Vague Vague

ConclusionLocal

rate/adjustment is recommended.

Local rate/adjustment is

recommended.

Use of ITE methods may be appropriate.

1(ITE, 2004, p. 21), 2(Clifton et al, 2012)

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Testing

• Test adjustment method using new locations

• Data collected at 34 establishments • Vehicle Counts, PM Peak, April-May

2012

Convenience Market (Open

24-hours)Drinking Place

High-Turnover (Sit-Down)

RestaurantsLU (851) LU (925) LU (932)

Sample Size 10 12 12

Mean Squared Error

UCA 38 10 29

ITE 1120 30 33

Average Percent Error

UCA 32% 31% 68%

ITE 195% 119% 63%

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Resulting Adjustments

All Trip Ends (pooled)

Retail

Residential

Single-Family

Multifamily

Entertainment/Recreational

Service (non-restaurant)

Restaurant

Office

A B C VehOcc

Mode Share

9 tables

27 models

3 methods

of adjustme

nt