California Smart Growth

Transcription

1 California Smart Growth Trip Generation Rates Study Kevan Shafizadeh, Ph.D., P.E., PTP, PTOE Associate Professor and Chair, Dept. of Civil Engineering California State University, Sacramento ITS Davis Seminar Friday, February 15, 2013 Research Team Susan Handy, Ph.D. Principal Investigator Professor, Environmental Science and Policy, UC Davis; Director, Sustainable Transportation Center, UC Davis; Deputy Director, ULTRANS UC Davis Robert J. Schneider, Ph.D., AICP Post Doctoral Researcher, UC Davis Now: Asst. Professor, Univ. of Wisconsin, i Milwaukee 2 1

2 Overview Background Definitions Study Motivation Data Collection Data Analysis Smart Growth Trip Generation Adjustment Tool Conclusions Acknowledgements 3 BACKGROUND 4 2

3 Definitions Smart Growth Site: Many jobs, residents, and activities nearby; pedestrian, bicycle, and transit modes are common. Targeted Land Use: The distinct land use on a site that was isolated for data collection 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) 5 Study Motivation California Environmental Quality Act (CEQA), requires developers in CA to estimate the transportation impacts of proposed developments. The guidance used most often for estimating trip generation is the Institute oftransportation Engineers (ITE) Trip Generation Handbook. 6 3

4 ITE Trip Generation Method Linear Regression: where T = 0.46(X) T is the Avg Vehicle Trip Ends X is a dependent variable, such as gross square footage or number of dwelling units Source: ITE, Trip Generation Manual, 8 th Edition, ITE Trip Generation Method Source: ITE, Trip Generation Manual, 8 th Edition,

5 Study Motivation Research suggests that vehicle use is generally lower at smart growth developments Authors (Year) Study Locations General Findings Kimley Horn & Associates (2009) Arrington & Cervero (2008) 16 Infill Study Sites AM peak trips were 27% lower & (Los Angeles, San Diego, PM peak trips were 28% lower and San Francisco Regions) than ITE for 3 mid rise apartments AM peak trips were 50% lower & PM peak trips were 50% lower than ITE for 4 general office buildings AM peak trips were 35% lower & PM peak trips were 26% lower than ITE for 2 quality restaurants 17 TOD Study Sites Weekday trips were 44% lower than ITE (Philadelphia, Portland, DC, AM peak trips were 49% lower than ITE and San Francisco regions) PM peak trips were 48% lower than ITE... but forecasting the effects on traffic remain challenging. 9 Study Motivation Using the ITE Trip Generation methodology on smart growth projects likely over estimates vehicle trips. Result: Mitigation measures that over emphasize vehicle needs while under supplying appropriate transit, bicycle, and pedestrian facilities. ITE Trip Generation rates remain widely used in practice and is based on large amount of data. How can they be modified or adjusted for smart growth locations? 10 5

6 Suburban Site (ITE Baseline) Image source: Google Earth Brentwood, CA 11 Suburban Site (ITE Baseline) Image source: Google Earth Place pneumatic tubes at driveway entrances Brentwood, CA 12 6

7 Suburban Site (ITE Baseline) Image source: Google Earth Problem: Pedestrians (& transit users) may not use driveways Brentwood, CA 13 Suburban Site (ITE Baseline) Image source: Google Earth Problem: Pedestrians (& transit users) & bicyclists may not be detected at driveways Brentwood, CA 14 7

8 Suburban Site (ITE Baseline) Image source: Google Earth Common Brentwood, CA 15 Suburban Site (ITE Baseline) Rare* Image source: Google Earth Brentwood, CA *Pedestrians, bicyclists, and public transit users are found in all environments, including suburban and rural areas: Some people don t own cars, can t drive, choose to use non auto modes for enjoyment & environmental reasons, etc. 16 8

9 Smart Growth Site Image source: Google Earth Sacramento, CA 17 Smart Growth Site Image source: Google Earth Place pneumatic tubes at parking structure entrances Sacramento, CA 18 9

10 Smart Growth Site Common Image source: Google Earth Problem: Pedestrians (& transit users) & bicyclists are common Sacramento, CA 19 Smart Growth Site Image source: Google Earth Problem: People park onstreet & in other garages Sacramento, CA 20 10

11 Smart Growth Site Image source: Google Earth Problem: People park in garage, but go to other buildings Sacramento, CA 21 Smart Growth Site Image source: Google Earth Problem: Multi use building with internal doors Sacramento, CA 22 11

12 Smart Growth Site Image source: Google Earth Study Method: Count and survey at all doors to a specific land use. Sacramento, CA 23 DATA COLLECTION 24 12

13 Data Collection Overview Site Selection Criteria Location of Sites Trip generation data collected at 23 smart growth sites in California during Spring 2012: Door Counts Intercept Surveys 25 Site Selection Smart Growth Criteria Mostly developed within 0.5 miles of site Mix of land uses within 025miles 0.25 of site > 6,000 residents or > 1,000 jobs within 0.5 miles of site Frequent transit service Designated bicycle facilities within two blocks > 50% sidewalk coverage on streets within 0.25 miles of the site 26 13

14 Site Selection Transferability Criteria Common LU Types: mid or high rise apartment, general office building, retail, coffee shop No special attractors nearby (e.g., stadiums, military bases, commercial airports, tourist attractions) At least 80% occupied and two years old Efficiency Criteria Advancepermission from property managers Not too many doorways Sufficient activity to obtain a sufficient number of intercept surveys in one day 27 Sacramento Region Study Sites 28 14

15 San Francisco Region Study Sites 29 Los Angeles Region Study Sites 30 15

16 181 Second Avenue, San Mateo 1 Door Counter at back entrance 1 Surveyor at 2 nd level garage entrance 1 Door Counter at 2 nd level garage g entrance 1 Surveyor at 1 st level garage entrance (rotated to back entrance) 1 Door Counter at 1 st levelgarage entrance 1 Surveyor at main 2 nd Ave. entrance 1 Door Counter at main 2 nd Ave. entrance Source: Google Earth Single Use Site: 181 Second Avenue, San Mateo 31 Fruitvale Station, 3100 E. 9 th Street, Oakland 1 Surveyor at Office Depot Entrance 1 Surveyor at K&G Fashion Entrance 1 Door Counter for both Office Depot and K&G Fashion 1 Door Counter at Starbucks entrance 1 Surveyor at Starbucks entrance Multi Use Site: Fruitvale Station, Oakland Source: Google Earth 32 16

17 30 Targeted Uses at 23 Study Sites ( ) ID Site Name Primary Address City Mid to High Density Resident tial Sansome 343 Sansome Stret San Francisco Sansome 343 Sansome Stret San Francisco Oakland City Center 1333 Broadway Oakland Oakland City Center 1333 Broadway Oakland Oakland City Center 1333 Broadway Oakland Fruitvale Station 3100 E. 9th Street Oakland Fruitvale Station 3100 E. 9th Street Oakland Sakura Crossing 235 S. San Pedro Street Los Angeles Artisan on 2nd 601 E. Second Street Los Angeles Victor on Venice Venice Boulevard Los Angeles Pegasus 612 S. Flower Street Los Angeles Paseo Colorado 280 E. Colorado Boulevard Pasadena The Sierra Oak Street Oakland Grand Avenue Grand Avenue Oakland Archstone at Del Mar Station Arroyo Parkway Pasadena 223 Office cial Retail Goods Commer Coffee/D Donut Shop Targeted Uses at 23 Study Sites ( ) High Density ntial Mid to Residen ID Site Name Primary yaddress y City 12.1 Terraces at Emery Station 5855 Horton Street Emeryville Holly Street Village 151 E. Holly Street Pasadena Emery Station East 5885 Hollis Street Emeryville Broadway Grand 438 W. Grand Avenue Oakland Broadway Grand 438 W. Grand Avenue Oakland Terraces Apartment Homes 375 E. Green Street Pasadena Second Avenue 181 2nd Avenue San Mateo Argenta 1 Polk Street San Francisco Charles Schwab Building 211 Main Street San Francisco Park Tower th Street Sacramento Park Tower th Street Sacramento Fremont Building th Street Sacramento Convention Plaza rd Street San Francisco Convention Plaza rd Street San Francisco Park Plaza 1303 J Street Sacramento 710 Total study locations in general use category Office rcial Retail Goods Comme /Donut Shop Coffee/ 34 17

18 Door Counts 35 Door Counts Inbound vs Outbound, Male & Female at every door 5 minute intervals over 3 hours to identify peak hour Total of 31,515 individuals counted People who parked in garage but did not go to targeted use were not included in further analysis 36 18

19 Convention Plaza Office Building, San Francisco Peak Hour: 4:50 5:49 p.m. 491 Entries + Exits Total Site Entries & Exits (Rolling 1 hour intervals) Intercept Surveys 38 19

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21 Intercept Surveys 3,371 individuals surveyed 61% of 5,501 individuals approached 5,170 trips recorded 41 Intercept Surveys 42 21

22 DATA ANALYSIS 43 Person Trip Analysis 1. Quantified peak hour person trips at each study location 2. Determined mode share at each door during each three hour data collection period Weighted door mode share by direction (inbound vs. outbound) Weighted door mode share by gender 3. Allocated peak hour person trips by mode at each door 4. Calculated peak hour person trips by mode for all study locations 44 22

23 Results: Person Trips by Mode (All study sites combined.) Transit (23%) Bicycle (3%) AM Trip Mode Share Pedestrian (29%) Transit (19%) Bicycle (2%) PM Trip Mode Share Pedestrian (26%) Automobile (45%) Automobile (53%) 45 PM Peak Hour Person Trips % 35% 39% 5% 9% Bicycle Transit Pedestrian Automobile Specific PM Peak Examples: San Francisco Sacramento Los Angeles 50 25% 61% 19% Sansome, SF (Office) 25% Park Tower, Sacramento (Coffee) 78% Artisan on 2nd, LA (Residential) 46 23

24 Results: Vehicle Trip Analysis (ITE Estimated Vehicle Trips vs. Actual Vehicle Trips) 1. Converted peak hour person trips to vehicle trips using vehicle occupancy fromsurveys 2. Calculated ratio of ITE estimated vehicle trips vs. Actual vehicle trips: ITE estimated veh trips Actual veh trips 47 Results: Vehicle Trip Analysis (ITE Estimated Vehicle Trips vs. Actual Vehicle Trips) On average, ITE vehicle trip estimates were 2.3 times higher than actual vehicle counts in the AM peak hour On average, ITE vehicle trip ti estimates t were 2.4 times higher than actual vehicle counts in the PM peak hour 48 24

25 Results: Vehicle Trip Analysis (ITE Estimated Vehicle Trips vs. Actual Vehicle Trips) Differences by land use category: Office: ITE averaged: 2.9 times more vehicle trips in AM 3.2 times more vehicle trips in PM Residential: ITE averaged: 1.1 times more vehicle trips in AM 14times 1.4 more vehicle trips in PM Coffee: ITE averaged: 2.6 times more vehicle trips in AM 1.2 times more vehicle trips in PM 49 PM Peak Hour Vehicle Trip Examples X 300 PM Pe eak Hour Vehicle Trips X 1.4 X 0 ITE Actual ITE Actual ITE Actual 343 Sansome, SF (Office) Park Tower, Sacramento (Coffee) Artisan on 2nd, LA (Residential) 50 25

26 PM Peak Hour Vehicle Trip Examples Converted to Vehicle Person Trips 500 PM Peak Hour Au tomobile Person Trips X ITE Actual ITE Actual ITE Actual 343 Sansome, SF (Office) 3.5 X Park Tower, Sacramento (Coffee) 1.4 X Artisan on 2nd, LA (Residential) (Obtained by multiplying by average vehicle occupancy.) 51 Most of the Difference between ITE Estimated Person Trips and Actual Person Trips was due to Mode Share 500 PM Peak Hour Person Trips X 0.9 X Bicycle Transit Pedestrian Automobile 1.1 X (Not a reduction in overall trip activity.) 50 0 ITE Actual ITE Actual ITE Actual 343 Sansome, SF (Office) Park Tower, Sacramento (Coffee) Artisan on 2nd, LA (Residential) 52 26

27 Most of the Difference between ITE Estimated Person Trips and Actual Person Trips was due to Mode Share On average, ITE estimated person trips were: 1.1 times higher than actual person trips in the AM peak hour 1.3 times higher than actual person trips ti in the PM peak hour 53 SMART GROWTH TRIP GENERATION ADJUSTMENT TOOL 54 27

28 Sites Used for Model Development Smart Growth Criteria Mostly developed within 0.5 miles of site Mix of land uses within 0.25 miles of site > 6,000 residents or > 1,000 jobs within 0.5 miles of site Frequent transit service Designated bicycle facilities within two blocks > 50% sidewalk coverage on streets within 0.25 miles of the site Transferability Criteria No special attractors nearby (e.g., stadiums, military bases, commercial airports, tourist attractions) At least 80% occupied and two years old 55 Sites Used for Model Development 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)

29 Database Development: Added Variables Site Land Use Category Office landuse Residential land use Service (Coffee Shop) land use Density Category Number of jobs within one half mile of study site Number of residents within one half mile of study site Land Use Mix Category Number of commercial retail and service properties within onequarter mile of study site. Number of different categories of retail and service properties within one quarter mile of study site. 57 Database Development: Added Variables Transportation Category Metered parking on streets adjacent to study site Proportion of arterial and collector roadways with designated bicycle facilities within one half mile of study site Study site is located within one half mile of a rail station Metered parking adjacent to site Socioeconomics Category Proportion of housing units within one half mile of study site that are rented Proportion of population within one half mile of study site that is younger than age 15 Proportion of population within one half mile of study site that is female Proportion of households within one half mile of study site that do not own a motor vehicle 58 29

30 Model Development: Dependent Variable Natural log transformation of the ratio of Actual Vehicle Trips to ITE estimated vehicle trips: ln actual veh trips ITE estimated veh trips 59 One Step Model: Linear Regression Attempted to identify singular variables most strongly associated with reduced trips Challenge: many SG variables are highly correlated It is likely that many SG variables are working together collectively, each playing small roles in the mode shift 60 30

31 Two Step Model: Factor Analysis with Linear Regression Model Factor Analysis Identifies smart growth variables that may be working together Quantifies the cumulative impact of this set of variables 61 Variable Factor Analysis: Smart Growth Factor 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

32 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 Coefficient t value p value Coefficient 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) 63 Example Central City Association of LA (PM): Smart Growth Factor Standardized Variable Coefficient Value Value* Factor 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 mi. (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 Smart Growth Factor (SGF) * The standardized value is calculated by subtracting the mean and dividing by the standard deviation of variable values from the 50 PM analysis sites

33 e Example Central City Association of LA (PM): Regression Model Model: ActualTrips ln ITETrips 0.155* SGF 0.529* OfficeLU 0.744* CoffeeLU 0.079* MXD 0.311* University Sample Calculation: Based on SG characteristics listed in previous slide e 0.155* * * * * If ITE method estimates 200 vehicle trips, this model suggests that (0.276*200) = 55 of those trips will be made by vehicle. 65 High & Low Examples (PM model) Office project with highest value SGF in sample = 2.41 Ratio actual/ite estimated is % vehicle trip reduction Office project with lowest value SGF in sample = 1.44 Ratio actual/ite estimated is % vehicle trip reduction Residentialproject withlowest value SGF in sample= Ratio actual/ite estimated is % vehicle trip reduction 66 33

34 AM Model Validation AM Model: 7 of 11 model predictions within 50% of observed ID Site Name City General LU Category AM Model Output (Actual/ITE) Observed AM (Actual/ITE) AM Model Observed Central City Association of Los Angeles Los Angeles, CA Office Horizon San Diego, CA Residential Atria San Diego, CA Residential Archstone Fox Plaza San Francisco, CA Residential Bong Su San Francisco, CA Restaurant Berkeleyan Apartments Berkeley, CA Residential Acton Courtyard Berkeley, CA Restaurant Sansome San Francisco, CA Coffee Broadway Grand Oakland, CA Residential Park Tower Sacramento, CA Coffee Convention Plaza San Francisco, CA Coffee PM Model Validation PM Model: 7 of 14 model predictions within 50% of observed General LU Category Observed PM Actual/ITE PM Model 1 Observed ID Site Name City PM Model 1 Output Central City Association of Los Angeles Los Angeles, CA Office Horizon San Diego, CA Residential Atria San Diego, CA Residential Archstone Fox Plaza San Francisco, CA Residential Bong Su San Francisco, CA Restaurant Berkeleyan Apartments Berkeley, CA Residential Touriel Building Berkeley, CA Residential Acton Courtyard Berkeley, CA Restaurant Bachenheimer Building Berkeley, CA Residential Fruitvale Station Oakland, CA Coffee Shop Paseo Colorado Pasadena, CA Retail Broadway Grand Oakland, CA Residential Park Tower Sacramento, CA Coffee Shop Convention Plaza San Francisco, CA Coffee Shop

35 AM Model Validation 2.50 AM Model Output vs. Observed Values 2.00 bserved Values (Actual/ITE) Ob Line shows where model = observed Model Values (Actual/ITE) 69 PM Model Validation 2.50 PM Model Output vs. Observed Values Busy PM coffee shop in auto oriented shopping complex 2.00 bserved Values (Actual/ITE) Ob Line shows where model = observed Model Values (Actual/ITE) 70 35

36 Model Development: Big Picture Study sites must be in smart growth locations Tested two approaches: 1) one step and 2) two step Iterative modeling process used to select explanatory variables in final model Final model balance theory and practice. 71 This study: Conclusions and Implications 1. Provides additional evidence that ITE Trip Generation does not address smart growth contexts. 2. Works to complement the existing ITE Trip Generation method. 3. Provides a method for practitioners to apply basic rate adjustments at smart growth sites. 4. Creates the foundation for multimodal person trip p database. 5. Informs national trip generation practice 6. Is being proposed as part of a revision to the ITE Trip Generation Handbook

37 Acknowledgements California Department of Transportation Terry Parker, Project Manager Data collection/methodology Brian Bochner, PE, PTP, PTOE, Texas Transportation Institute Benjamin Sperry, PhD., Texas Transportation Institute Data Collection Ewald & Wasserman Research Consultants Gene Bregman & Associates Manpower, Inc. Image source: Benjamin Sperry 73 Acknowledgements Other: Existing methodology review, Data Management, QA/QC Richard Lee, Ph.D., UC Davis Deb Niemeier, Ph.D., UC Davis Josh Miller, UC Davis Rachael Maiss, UC Davis Calvin Thigpen, UC Davis Mary Madison Campbell, UC Davis Property managers and developers Image source: Benjamin Sperry 74 37

38 Acknowledgements Caltrans Division of Research & Innovation (DRI) Federal Highway Administration (FHWA) Practitioner Panel: Marc Birnbaum, Caltrans Brian Bochner, Texas Transportation Institute Ann Cheng, TransForm Charlie Clouse, TPG Inc. Paul Crabtree, Townworks + DPZ Pat Gibson, Gibson Transportation Consulting Samir Haijiri, City of San Diego Pang Ho, PH Associates Don Hubbard, Parsons Brinckerhoff Eric Ruehr, VRPA Technologies, Inc. Edward Sullivan, Economic & Planning Systems 75 Questions & Discussion For more information, see the project website: smart growth trip generation 76 38