Advances in Estimating the Transportation Impact of Development for Urban Locations

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Too often our transportation systems detract from the very communities they aim to serve. Many of the hurdles keeping local and regional areas from developing more sustainable and livable environments are the result of institutionalized engineering practice. The use of the Institute of Transportation Engineers Trip Generation Handbook has been known to hamper the ability for jurisdictions to build new developments in multimodal (e.g., transit, pedestrian, and bicycle), urban contexts without overestimating the vehicle trips generated, thereby limiting the implementation of emissions-reducing, mixed-use, developments supporting multimodal travel. This webinar provides an overview of the current trip generation estimation practice and describes both alternative and substitute methods to estimate multimodal, urban-sensitive trip generation rates for new development.

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  • Cite JTLU article
  • Cite JTLU article
  • UCA improves estimates for Convenience Markets and Drinking Places
    UCA and ITE estimates for Restaurants are similar
  • 4 sites: met all criteria, 16 met all but one criteria (PRIMARY)
    23 sites met most of the criteria (SECONDARY)
    5 sites did not meet criteria (FULL ANALYSIS SET)
  • Census
    LEHD
    TOD database
  • UCA improves estimates for Convenience Markets and Drinking Places
    UCA and ITE estimates for Restaurants are similar
  • Advances in Estimating the Transportation Impact of Development for Urban Locations

    1. 1. Advances in Estimating the Transportation Impact of Development for Urban Locations An IBPI Webinar Kristina M. Currans Civil and Environmental Engineering, Portland State University Adviser: Dr. Kelly J. Clifton 1
    2. 2. Outline • Background • Methods of Adjustments for Urban Locations – Urban Context Adjustment (UCA) – Smart-Growth Trip Generation (SGTG) Adjustment – Household Travel Survey (HTS) Adjustment • Conclusions 2
    3. 3. 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 3 1(ITE, 2004; ITE, 2012)
    4. 4. 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 4 *Graphic from (ITE, 2008) *
    5. 5. 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 5
    6. 6. 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 6
    7. 7. 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 7
    8. 8. URBAN CONTEXT ADJUSTMENT (UCA) MODEL Portland State University, OTREC 8
    9. 9. 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) 9
    10. 10. 10
    11. 11. 𝐴𝐷𝐽 = 𝛽𝑐𝑜𝑛𝑠 + 𝛽 𝐵𝐸 ∗ 𝐵𝐸 + 𝛽𝑟𝑒𝑠𝑡 ∗ 𝑅𝐸𝑆𝑇 + 𝛽𝑐𝑜𝑛𝑣 ∗ 𝐶𝑂𝑁𝑉 Dependent Variables: 𝐴𝐷𝐽 = 𝑉𝐸𝐻 𝑇𝑅𝐼𝑃𝑆 𝑇𝐺𝑆,𝐿𝑈 − 𝑉𝐸𝐻 𝑇𝑅𝐼𝑃𝑆𝐼𝑇𝐸,𝐿𝑈 ≡ Difference in vehicle trip rates Independent Variables: BE ≡ Average of built environment (BE) measures within 1/2 mile buffer 𝑅𝐸𝑆𝑇 = 1, if ITE Land Use = 932: High−Turnover Restaurant 0, if ITE Land Use ≠ 932: High−Turnover Restaurant 𝐶𝑂𝑁𝑉 = 1, if ITE Land Use = 851:Convenience Market 0, if ITE Land Use ≠ 851:Convenience Market Note: Drinking places are the base case for the model Urban Context Adjustment (UCA) Models 11
    12. 12. 12 Built Environment (𝑩𝑬) 𝜷 𝑩𝑬 𝜷 𝒄𝒐𝒏𝒗 (𝑪𝑶𝑵𝑽=1) 𝜷 𝒓𝒆𝒔𝒕 (𝑹𝑬𝑺𝑻=1) 𝜷 𝒄𝒐𝒏𝒔 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)
    13. 13. ULI in Metro Region
    14. 14. 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 + ADJ New Adjusted Rate = 52.4 + (– 34.9) = 17.5 trip ends/1000 SQFT 14 𝐴𝐷𝐽 = 𝛽𝑐𝑜𝑛𝑠 + 𝛽 𝐵𝐸 ∗ 𝐵𝐸 + 𝛽𝑟𝑒𝑠𝑡 ∗ 𝑅𝐸𝑆𝑇 + 𝛽𝑐𝑜𝑛𝑣 ∗ 𝐶𝑂𝑁𝑉 𝐴𝐷𝐽 = 0.7 + −3.3 ∗ 𝑈𝐿𝐼 + 7.4 ∗ 𝑅𝐸𝑆𝑇 + −26.0 ∗ 𝐶𝑂𝑁𝑉 𝐴𝐷𝐽 = 0.7 + −3.3 ∗ 𝟐. 𝟗 + 7.4 ∗ 𝟎 + −26.0 ∗ 𝟏 = −𝟑𝟒. 𝟗 𝐴𝐷𝐽 = 𝛽𝑐𝑜𝑛𝑠 + 𝛽 𝐵𝐸 ∗ 𝐵𝐸 + 𝛽𝑟𝑒𝑠𝑡 ∗ 𝑅𝐸𝑆𝑇 + 𝛽𝑐𝑜𝑛𝑣 ∗ 𝐶𝑂𝑁𝑉 𝐴𝐷𝐽 = 0.7 + −3.3 ∗ 𝑈𝐿𝐼 + 7.4 ∗ 𝑅𝐸𝑆𝑇 + −26.0 ∗ 𝐶𝑂𝑁𝑉 𝐴𝐷𝐽 = 𝛽𝑐𝑜𝑛𝑠 + 𝛽 𝐵𝐸 ∗ 𝐵𝐸 + 𝛽𝑟𝑒𝑠𝑡 ∗ 𝑅𝐸𝑆𝑇 + 𝛽𝑐𝑜𝑛𝑣 ∗ 𝐶𝑂𝑁𝑉 ~[ -66% of ITE’s rate ]
    15. 15. Testing • Test adjustment method using new locations • Data collected at 34 establishments • Vehicle counts, PM Peak, April-May 2012 15 Convenience Market (Open 24- hours) Drinking Place High-Turnover (Sit-Down) Restaurants LU (851) LU (925) LU (932) Sample Size 10 12 12 Average Percent Error UCA 32% 31% 68% ITE 195% 119% 63%
    16. 16. 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 16
    17. 17. SMART-GROWTH TRIP GENERATION (SGTG) MODEL University of California, Davis for Caltrans 17
    18. 18. 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 18
    19. 19. 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 19
    20. 20. Example Application Download the Excel toolkit here: http://ultrans.its.ucdavis.edu/projects/smart-growth-trip-generation20
    21. 21. 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, campusesDownload the Excel toolkit here: http://ultrans.its.ucdavis.edu/projects/smart-growth-trip-generation21
    22. 22. Example Application Outputs: • Smart growth factor – Composite index of all built environment measure • Estimated AM/PM peak hour vehicle trips adjusted for urban context Download the Excel toolkit here: http://ultrans.its.ucdavis.edu/projects/smart-growth-trip-generation22
    23. 23. 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 23 1(Handy et al, 2013)
    24. 24. Benefits and Limitations 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 24
    25. 25. HOUSEHOLD TRAVEL SURVEY (HTS) ADJUSTMENTS Civil and Environmental Engineering, Portland State University 25
    26. 26. 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 26
    27. 27. Primary Data 27
    28. 28. Household Travel Surveys (HTS) 28
    29. 29. 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 29
    30. 30. Resulting Adjustments • Simple mode share table ACTIVITY DENSITY %MODESHARE INTERSECTION DENSITY %AUTOSHARE POPULATION DENSITY %AUTOSHARE • Regressions with different built environment measures, control for addition travel information 30
    31. 31. 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 2,500 sqft 131 Vehicle Trip Ends *Graphic from (ITE, 2008) *
    32. 32. Example Application (2) Convert ITE’s vehicle trips into person trips 131 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑡𝑟𝑖𝑝𝑠 ∗ 1.0 𝑝𝑒𝑟𝑠𝑜𝑛𝑠 𝑝𝑒𝑟 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 100% 𝑎𝑢𝑡𝑜𝑚𝑜𝑏𝑖𝑙𝑒 𝑚𝑜𝑑𝑒 𝑠ℎ𝑎𝑟𝑒 = 131 𝐼𝑇𝐸 𝑝𝑒𝑟𝑠𝑜𝑛 𝑡𝑟𝑖𝑝𝑠 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑇𝑟𝑖𝑝𝑠𝐼𝑇𝐸 ∗ 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑂𝑐𝑐𝑢𝑝𝑎𝑛𝑐𝑦𝐼𝑇𝐸 𝐴𝑢𝑡𝑜𝑚𝑜𝑏𝑙𝑒 𝑀𝑜𝑑𝑒 𝑆ℎ𝑎𝑟𝑒𝐼𝑇𝐸 = 𝑃𝑒𝑟𝑠𝑜𝑛 𝑇𝑟𝑖𝑝𝑠𝐼𝑇𝐸 32
    33. 33. 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 𝑃𝑒𝑟𝑠𝑜𝑛 𝑇𝑟𝑖𝑝𝑠𝐼𝑇𝐸 ∗ 𝐴𝑢𝑡𝑜𝑚𝑜𝑏𝑖𝑙𝑒 𝑀𝑜𝑑𝑒 𝑆ℎ𝑎𝑟𝑒 𝑈𝑟𝑏𝑎𝑛 𝐶𝑜𝑛𝑡𝑒𝑥𝑡 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑂𝑐𝑐𝑢𝑝𝑎𝑛𝑐𝑦 𝑈𝑟𝑏𝑎𝑛 𝐶𝑜𝑛𝑡𝑒𝑥𝑡 = 𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑇𝑟𝑖𝑝𝑠 𝑈𝑟𝑏𝑎𝑛 𝐶𝑜𝑛𝑡𝑒𝑥𝑡 131 𝐼𝑇𝐸 𝑝𝑒𝑟𝑠𝑜𝑛 𝑡𝑟𝑖𝑝𝑠 ∗ 91% automobile mode share 1.16 𝑝𝑒𝑟𝑠𝑜𝑛𝑠 𝑝𝑒𝑟 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 = 103 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑡𝑟𝑖𝑝𝑠 33
    34. 34. 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) 34
    35. 35. 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 35
    36. 36. Benefits and Limitations 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 36
    37. 37. CONCLUSIONS 37
    38. 38. 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 38 1(Clifton et al, 2013; Schneider et al, 2013)
    39. 39. Questions? Kristina M. Currans Civil Engineering Portland State University kcurrans@pdx.edu 39
    40. 40. BIBLIOGRAPHY 40
    41. 41. Bibliography ARUP (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. 41
    42. 42. Bibliography Institute 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.aspx Puget 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 42
    43. 43. BONUS SLIDES 43
    44. 44. 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) 44
    45. 45. Establishing a Need for Local Rates2 45 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 = 20.8 ITERATE ± SD.= 31.0 - 73.8 TGSRATE = 4.9 ITERATE ± SD.= 3.3 - 19.4 TGSRATE = 12.3 ITERATE ± SD.= 2.0 - 20.3 At least 1 study site that falls above the ITE weighted average or equation, and 1 that falls below; OR All 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 1(ITE, 2004, p. 21), 2(Clifton et al, 2012)
    46. 46. Testing • Test adjustment method using new locations • Data collected at 34 establishments • Vehicle Counts, PM Peak, April-May 2012 46 Convenience Market (Open 24- hours) Drinking Place High-Turnover (Sit-Down) Restaurants LU (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%
    47. 47. Resulting Adjustments All Trip Ends (pooled)     Retail     Residential     Single-Family     Multifamily     Entertainment/Recreational     Service (non-restaurant)     Restaurant     Office     A B C Veh Occ Mode Share 9 tables 27 models 3 methods of adjustment 47

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