Method to Adjust ITE Vehicle Trip-Generation Estimates in Smart-Growth Areas Robert J. Schneider, Kevan Shafizadeh, & Susan L. Handy University of

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1 Method to Adjust ITE Vehicle Trip-Generation Estimates in Smart-Growth Areas Robert J. Schneider, Kevan Shafizadeh, & Susan L. Handy University of Wisconsin-Milwaukee, CSU Sacramento, & UC Davis TRB Innovations in Travel Modeling Conference April

2 Overview Definitions Need for adjustments to ITE Other adjustment methods Development of adjustment model in CA Considerations & future research Image source: Benjamin Sperry 2

3 Definitions 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). 3

4 How many vehicle trips are generated by a specific land use? 4

5 Need for Adjustments to ITE Trip Generation 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. 5

6 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 6

7 Need for Adjustments to ITE Trip Generation ITE-Estimated Vehicle-Trips vs. Actual Vehicle-Trips at 30 CA SG Sites 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 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 ,

8 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? 8

9 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) 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: ,

10 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 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: ,

11 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 11

12 Would it work to apply a single adjustment factor to ITE estimates all Smart Growth sites? 12

13 Example Site 1: 343 Sansome, SF (Office) 13

14 Example Site 2: Park Tower, Sacramento (Coffee) 14

15 Example Site 3: Artisan on 2 nd, LA (Residential) Photo by Ben Sperry, Texas A&M Transportation Institute 15

16 PM Peak Hour Vehicle-Trips PM Peak Hour Vehicle-Trip Examples X X 1.4 X 0 ITE Actual ITE Actual ITE Actual 343 Sansome, SF (Office) Park Tower, Sacramento (Coffee) Artisan on 2nd, LA (Residential) 16

17 PM Peak Hour Vehicle-Trips PM Peak Hour Vehicle-Trip Examples X Study Motivation: What characteristics account for differences in ITE overestimates within Smart Growth areas? X 1.4 X 0 ITE Actual ITE Actual ITE Actual 343 Sansome, SF (Office) Park Tower, Sacramento (Coffee) Artisan on 2nd, LA (Residential) 17

18 Average Discrepancy (ITE Vehicle Trips/Actual Vehicle Trips) ITE Underestimates ITE Overestimates Average Discrepancy by LU Category (CA Smart Growth Sites) AM (8 Sites) PM (9 Sites) AM (4 Sites) PM (4 Sites) AM (12 Sites) Office Coffee Shop Residential PM (11 Sites) 18

19 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 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,

20 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? 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,

21 Development of California Smart-Growth Trip Generation Model 21

22 Criteria for Smart Growth Model Application 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, J) 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 22

23 Sites Used for Model Development (Los Angeles, San Diego, San Francisco, and Sacramento Regions) AM Model PM Model Residential Land Use Office Land Use Coffee/Donut Land Use 3 3 MXD Land Use Retail Land Use 0 3 Other Land Use 1 1 Total Sites 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) 23

24 Model Development: Dependent Variable ln actual veh trips ITE estimated veh trips 24

25 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) 25

26 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 26

27 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 27

28 Factor Analysis: Smart Growth Factor Variable Coefficient* Population within 0.5 miles (000s) Jobs within 0.5 miles (000s) Distance to center of CBD (in miles) Average building setback from sidewalk Metered parking within 0.1 miles (1=yes, 0 = no) Number of bus lines within 0.25 miles Number of rail lines within 0.5 miles Percent of site area covered by surface parking *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. 28

29 Linear Regression: Final AM and PM Peak Hour Models 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 Office land use (1 = yes, 0 = no) Coffee shop land use (1 = yes, 0 = no) Mixed-use development (1 = yes, 0 = no) Within 1 mi. of university (1 = yes, 0 = no) Constant Overall Model Sample Size (N) Adjusted R 2 -Value F-Value (Test value) 4.74 (p = 0.002) 4.99 (p = 0.001) 29

30 Linear Regression: Final AM and PM Peak Hour Models 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 Office land use (1 = yes, 0 = no) Coffee shop land use (1 = yes, 0 = no) Mixed-use development (1 = yes, 0 = no) Within 1 mi. of university (1 = yes, 0 = no) Constant Overall Model Sample Size (N) Adjusted R 2 -Value F-Value (Test value) 4.74 (p = 0.002) 4.99 (p = 0.001) 30

31 Linear Regression: Final AM and PM Peak Hour Models 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 Office land use (1 = yes, 0 = no) Coffee shop land use (1 = yes, 0 = no) Mixed-use development (1 = yes, 0 = no) Within 1 mi. of university (1 = yes, 0 = no) Constant Overall Model Sample Size (N) Adjusted R 2 -Value F-Value (Test value) 4.74 (p = 0.002) 4.99 (p = 0.001) Bold values indicate p-values <

32 High & Low Examples (PM Model) Office project with highest value SGF in sample = 2.41 Ratio actual/ite-estimated is % vehicle trip reduction from ITE Office project with lowest value SGF in sample = Ratio actual/ite-estimated is % vehicle trip reduction from ITE Residential project with lowest value SGF in sample = Ratio actual/ite-estimated is % vehicle trip reduction from ITE 32

33 PM Model Validation (N = 13) 33

34 PM Model Validation (N = 13) 34

35 Sneak Preview: Model Verification How well does the PM model work at a sample of sites in a different urban region? Portland, OR 35

36 Model Verification Observed versus Predicted Ratios to ITE Estimates: 20 Most Appropriate Portland Sites Image Source: Andrew McFadden, UC Davis 36

37 Model Verification ITE- and Model- Estimated Trips vs. Actual Trips: 20 Most Appropriate Portland Sites Image Source: Andrew McFadden, UC Davis 37

38 Modeling Considerations 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 38

39 Model Development: Big Picture Final models balance theory and practice Complement existing ITE Trip Generation method Two-step method was a key breakthrough 39

40 Spreadsheet Tool Downtown LA Example: 72% vehicle trip reduction from ITE during PM peak 40

41 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 41

42 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 Image source: Benjamin Sperry For more information, see project website: 42

43 Questions & Discussion For more information, see the project website: smart-growth-trip-generation 43

44 Factor Analysis: Smart Growth Factor 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). 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:

45 Factor Analysis: Smart Growth Factor Loadings Variable Loading Population within 0.5 miles (000s).538 Jobs within 0.5 miles (000s).781 Distance to center of CBD (in miles) Average building setback from sidewalk 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

46 San Francisco Region Study Sites 46

47 Los Angeles Region Study Sites 47

48 Sacramento Region Study Sites 48

49 Future Research: Model Improvement More data to refine models; test in other regions Need SG adjustments for more land uses 49

50 78 Sites in Portland, OR Data Source: Clifton, et al., Portland State University, Image Source: Andrew McFadden, UC Davis 50

51 Model Verification Observed versus Predicted Ratios to ITE Estimates: All 78 Sites Image Source: Andrew McFadden, UC Davis 51