California Smart Trip Generation Rates Studydownloads.ice.ucdavis.edu/ultrans/smartgrowthtripgen/ITS-Davis... · Trip Generation Rates Study ... 13.1 Holly Street Village 151 E. Holly

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California Smart GrowthTrip Generation Rates StudyTrip Generation Rates Study

Kevan Shafizadeh, Ph.D., P.E., PTP, PTOE

Associate Professor and Chair, Dept. of Civil Engineering

California State University SacramentoCalifornia 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 DavisN A t P f U i f Wi i Mil k– Now: Asst. Professor, Univ. of Wisconsin, Milwaukee

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Overview

• Background – Definitions – Study Motivation

• Data Collection• Data Analysis• Smart Growth Trip‐Generation Adjustment Tool

• Conclusions• Acknowledgements

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BACKGROUND

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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 collectionisolated 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)

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Study Motivation

• California Environmental Quality Act (CEQA), requires developers in CA to estimate the

f dtransportation impacts of proposed developments.

• The guidance used most often for estimating trip generation is the Institute of TransportationInstitute of Transportation Engineers (ITE) Trip Generation Handbook.

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ITE Trip Generation Method

• Linear Regression:

( )T = 0.46(X) – 14.01

where

• T is the Avg Vehicle Trip Ends• X is a dependent variable,

such as gross square footageor number of dwelling units

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Source: ITE, Trip Generation Manual, 8th Edition, 2008.

ITE Trip Generation Method

8Source: ITE, Trip Generation Manual, 8th Edition, 2008.

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Study Motivation

• Research suggests that vehicle use is generally lower at smart growth developments…

Authors (Year) Study Locations General Findings

Kimley Horn & Associates(2009)

16 Infill Study Sites(Los Angeles, San Diego,

and San Francisco Regions)

• AM peak trips were 27% lower & PM peak trips were 28% lower 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

... but forecasting the effects on traffic remain challenging.9

PM peak trips were 26% lower than ITE for 2 quality restaurants

Arrington & Cervero(2008)

17 TOD Study Sites (Philadelphia, Portland, DC, and 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

Study Motivation

• Using the ITE Trip Generation methodology on smart growth projects likely over‐estimates vehicle tripsvehicle 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?

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6

Suburban Site (ITE Baseline)

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Image source: Google Earth

Brentwood, CA


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Place pneumatic tubes at driveway entrances

Brentwood, CA

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Problem: Pedestrians (& transit users) may not use driveways

Brentwood, CA


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Problem: Pedestrians (& transit users) & bicyclists may not be

detected at driveways

Brentwood, CA

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15


Brentwood, CACommon

Suburban Site (ITE Baseline)Rare*

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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.

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Smart‐Growth Site

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Image source: Google EarthSacramento, CA

Smart‐Growth Site

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Image source: Google EarthSacramento, CAPlace pneumatic tubes at

parking structure entrances

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Smart‐Growth SiteCommon

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Image source: Google EarthSacramento, CAProblem: Pedestrians (& transit

users) & bicyclists are common

Smart‐Growth Site

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Image source: Google EarthSacramento, CAProblem: People park on‐

street & in other garages

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Smart‐Growth Site

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Image source: Google EarthSacramento, CAProblem: People park in garage,

but go to other buildings

Smart‐Growth Site

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Problem: Multi‐use building with internal doors

Sacramento, CA

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Smart‐Growth Site

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Study Method: Count and survey at all doors to a specific land use.

Sacramento, CA

DATA COLLECTION24

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Data Collection Overview

• Site Selection Criteria• Location of Sites• Trip generation data collected at 23 smart‐growth sitesin California during Spring 2012:– Door Counts – Intercept Surveys

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Site Selection

Smart Growth Criteria• Mostly developed within 0.5 miles of

site• Mix of land uses within 0 25 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

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> 50% sidewalk coverage on streets within 0.25 miles of the site

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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• No special attractors nearby (e.g., stadiums,

military bases, commercial airports, tourist attractions)

• At least 80% occupied and two years old

Efficiency Criteria• Advance permission from property

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Advance permission from property managers

• Not too many doorways• Sufficient activity to obtain a sufficient

number of intercept surveys in one day

Sacramento Region Study Sites

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15

San Francisco Region Study Sites

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Los Angeles Region Study Sites

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16

181 Second Avenue, San Mateo

1 Door Counter at back entrance

1 Door Counter at 2nd

level garage entrance

1 Surveyor at 1st level garage entrance

(rotated to back entrance)

1 Surveyor at 2nd level garage entrance

g g

1 Door Counter at 1st


Single‐Use Site: 181 Second Avenue, San Mateo

1 Door Counter at main 2nd Ave. entrance

1 Surveyor at main 2nd

Ave. entrance


Source: Google Earth31

Fruitvale Station, 3100 E. 9th Street, Oakland

1 Surveyor at K&G Fashion Entrance

1 Surveyor at Office Depot Entrance

K&G Fashion Entrance

1 Door Counter for both Office Depot and K&G Fashion

Multi‐Use Site: Fruitvale Station, Oakland

1 Surveyor at Starbucks entrance

1 Door Counter at Starbucks entrance

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Source: Google Earth

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30 Targeted Uses at 23 Study Sites

High‐Density

tial cial Retail Goods

Donut Shop

( )

ID Site Name Primary Address City Mid‐ to H

Resident

Office

Commer

Coffee/ D

1.1 343 Sansome 343 Sansome Stret San Francisco 710

1.2 343 Sansome 343 Sansome Stret San Francisco 936

2.1 Oakland City Center 1333 Broadway Oakland 710



3.1 Fruitvale Station 3100 E. 9th Street Oakland 867

3.2 Fruitvale Station 3100 E. 9th Street Oakland 936

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4.1 Sakura Crossing 235 S. San Pedro Street Los Angeles 223

5.1 Artisan on 2nd 601 E. Second Street Los Angeles 223

6.1 Victor on Venice 10001 Venice Boulevard Los Angeles 223

7.1 Pegasus 612 S. Flower Street Los Angeles 222

8.1 Paseo Colorado 280 E. Colorado Boulevard Pasadena 820

9.1 The Sierra6

311 Oak Street Oakland 223

10.1 180 Grand Avenue7

180 Grand Avenue Oakland 710

11.1 Archstone at Del Mar Station6 265 Arroyo Parkway Pasadena 223

30 Targeted Uses at 23 Study Sites

High‐Density

ntial

rcial Retail Goods

/Donut Shop

( )

y y

12.1 Terraces at Emery Station 5855 Horton Street Emeryville 223

13.1 Holly Street Village 151 E. Holly Street Pasadena 223

14.1 Emery Station East 5885 Hollis Street Emeryville 710

15.1 Broadway Grand 438 W. Grand Avenue Oakland 223

15.2 Broadway Grand 438 W. Grand Avenue Oakland 936

16.1 Terraces Apartment Homes 375 E. Green Street Pasadena 223

17.1 181 Second Avenue 181 2nd Avenue San Mateo 710

18 1 Argenta 1 Polk Street San Francisco 222

ID Site Name Primary Address City Mid‐ to

Residen

Office

Comme

Coffee/

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18.1 Argenta 1 Polk Street San Francisco 222

19.1 Charles Schwab Building 211 Main Street San Francisco 710

20.1 Park Tower7

980 9th Street Sacramento 710

20.2 Park Tower7

980 9th Street Sacramento 936

21.1 Fremont Building 1501 16th Street Sacramento 223

22.1 Convention Plaza7

201 3rd Street San Francisco 710

22.2 Convention Plaza7

201 3rd Street San Francisco 936

23.1 Park Plaza 1303 J Street Sacramento 710

12 9 3 6Total study locations in general use category

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Door Counts

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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• People who parked in garage but did not go to targeted use were

not included in further analysis

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19

500

600

Total Site Entries & Exits(Rolling 1‐hour intervals)

Convention Plaza Office Building, San Francisco

Peak Hour: 4:50‐5:49 p.m.

491 Entries + Exits

200

300

400

0

100

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Intercept Surveys

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20

39

40

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Intercept Surveys

• 3,371 individuals surveyed– 61% of 5,501 individuals approached

• 5,170 trips recorded

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Intercept Surveys

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DATA ANALYSIS

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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 each3. Allocated peak hour person trips by mode at each door

4. Calculated peak‐hour person trips by mode for all study locations

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Results: Person‐Trips by Mode(All study sites combined.)

PM Trip Mode ShareAM Trip Mode Share

Pedestrian(26%)

Transit(19%)

Bicycle(2%)

Pedestrian(29%)

Transit(23%)

Bicycle(3%)

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Automobile(53%)

Automobile(45%)

SpecificPM PeakExamples:S F i

35%

1%

250

300

350

Bicycle

Transit

Pedestrian

AutomobileSan FranciscoSacramentoLos Angeles

39%

5%100

150

200

PM Peak

Hour Person‐Trips

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25%

25%78%

61% 19%

9%

0

50

343 Sansome, SF(Office)

Park Tower,Sacramento (Coffee)

Artisan on 2nd, LA(Residential)

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Results: Vehicle‐Trip Analysis(ITE‐Estimated Vehicle Trips vs. Actual Vehicle Trips)

1. Converted peak‐hour person trips to vehicle trips using vehicle occupancy from surveysusing vehicle occupancy from surveys

2. Calculated ratio of ITE‐estimated vehicle‐trips vs. Actual vehicle‐trips:

veh tripsestimatedITE

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veh tripsActual

p


• On average, ITE vehicle‐trip estimates were 2.3 times higher than actual vehicle counts in the AM peak hour

O ITE hi l t i ti t• On average, ITE vehicle‐trip estimates were 2.4 times higher than actual vehicle counts in the PM peak hour

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• Differences by land use category:Offi ITE d


–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 • 1 4 times more vehicle trips in PM• 1.4 times more vehicle trips in PM

–Coffee: ITE averaged: • 2.6 times more vehicle trips in AM• 1.2 times more vehicle trips in PM

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PM Peak‐Hour Vehicle‐Trip Examples

350

400

5.8 X

150

200

250

300

eak Hour Vehicle‐Trips

50

0

50

100

ITE Actual ITE Actual ITE Actual




PM Pe

3.5 X1.4 X

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400

450

500

PM Peak‐Hour Vehicle‐Trip Examples Converted to Vehicle‐Person Trips

5.8 X

(Obtained by

150

200

250

300

350

400tomobile

Person‐Trips

multiplying by average vehicle

occupancy.)

0

50

100

150





PM Peak Hour Au

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3.5 X1.4 X

400

450

500

Bicycle

Transit

Most of the Difference between ITE‐Estimated Person‐Trips and Actual Person‐Trips was due to Mode Share

1.5 X

(Not a

150

200

250

300

350

400

Peak Hour Person‐Trips

Pedestrian

Automobile

(Not a reduction in overall

trip activity.)

0

50

100

150





PM P

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0.9 X1.1 X

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• On average, ITE‐estimated person‐trips

Most of the Difference between ITE‐Estimated Person‐Trips and Actual Person‐Trips was due to Mode Share

were:–1.1 times higher than actual person‐trips in the AM peak hour

– 1.3 times higher than actual t i i th PM k h

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person‐trips in the PM peak hour

SMART GROWTH TRIP‐GENERATION ADJUSTMENT TOOL

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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• > 6,000 residents or > 1,000 jobs within

Sites Used for Model Development

, , j0.5 miles of site

• Frequent transit service• Designated bicycle facilities within two

blocks• > 50% sidewalk coverage on streets

within 0.25 miles of the site

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Transferability Criteria• No special attractors nearby (e.g.,

stadiums, military bases, commercial airports, tourist attractions)

• At least 80% occupied and two years old

Sites Used for Model Development

AM Model PM Model

Residential Land Use 20 20Residential 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

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).

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Retail Land Use 0 3

Other Land Use 1 1

Total Sites 46 50

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Database Development: Added Variables

• Site Land Use Category

Office land use

• Land Use Mix Category

Number of commercial– Office land use

– Residential land use

– Service (Coffee Shop) land use

• Density Category

– Number of commercial retail and service properties within one‐quarter mile of study site.

– Number of different categories of retail and service properties within

– Number of jobs within one‐half mile of study site

– Number of residents within one‐half mile of study site

one‐quarter mile of study site.

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Database Development: Added Variables

• Transportation Category– Metered parking on streets

• Socioeconomics Category– Proportion of housing units

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

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

station– Metered parking adjacent

to site

study site that is female– Proportion of households

within one‐half mile of study site that do not own a motor vehicle

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Model Development: Dependent Variable

• Natural log transformation of the ratio of Actual Vehicle Trips to ITE‐estimated vehicleActual Vehicle Trips to ITE estimated vehicle trips:

veh tripsestimated ITE

veh tripsactualln

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One‐Step Model: Linear Regression

• Attempted to identify singular variables most strongly associated with reduced tripsstrongly 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

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Two‐Step Model: Factor Analysis with Linear Regression Model

• Factor AnalysisId tifi t th– Identifies smart growth variables that may be “working together”

– Quantifies the cumulative impact of this set ofimpact of this set of variables

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Factor Analysis: Smart Growth Factor

Variable Coefficient*

Population within 0.5 miles (000s) 0.099p ( )

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

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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.

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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 ‐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

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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)

Example ‐ Central City Association of LA (PM):Smart Growth Factor

Variable Coefficient Value

Standardized

Value* Factor

Population within 0.5 miles (000s) 0.099 13.072 0.492 0.049

Jobs within 0.5 miles (000s) 0.324 74.881 1.690 0.548

Distance to center of CBD (in miles) ‐0.138 0.089 ‐0.807 0.111

Average building setback from sidewalk ‐0.167 0.000 ‐0.657 0.110

Metered parking within 0.1 mi. (1=yes, 0 = no) 0.184 1.000 0.776 0.143

Number of bus lines within 0.25 miles 0.227 208.000 3.237 0.735

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Number of rail lines within 0.5 miles 0.053 4.000 ‐0.232 ‐0.012

Percent of site area covered by surface parking ‐0.080 0.000 ‐0.506 0.041

Smart Growth Factor (SGF) 1.723

* The standardized value is calculated by subtracting the mean and dividing by the standard deviation of variable values from the 50 PM analysis sites.

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• Model:

Trips ITE

Trips Actualln

Example ‐ Central City Association of LA (PM):Regression Model

• Sample Calculation:– Based on SG characteristics listed in previous slide

491.0*311.0*079.0*744.0*529.0*155.0 UniversityMXDCoffeeLUOfficeLUSGFe

491.00*311.00*079.00*744.01*529.0723.1*155.0 e

– If ITE method estimates 200 vehicle trips, this model suggests that (0.276*200) = 55 of those trips will be made by vehicle.

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276.0

High & Low Examples (PM model)

• Office project with highest value SGF in sample = 2.41– Ratio actual/ITE‐estimated is 0.24875% vehicle trip reduction– 75% vehicle trip reduction

• Office project with lowest value SGF in sample = ‐1.44– Ratio actual/ITE‐estimated is 0.451– 55% vehicle trip reduction

• Residential project with lowest value SGF in sample = 1 44• Residential project with lowest value SGF in sample = ‐1.44– Ratio actual/ITE‐estimated is 0.765– 23% vehicle trip reduction

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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

113.1 Central City Association of Los Angeles Los Angeles, CA Office 0.30 0.41 ‐0.10

114.1 Horizon San Diego, CA Residential 0.72 0.23 0.49

115.1 Atria San Diego, CA Residential 0.72 0.82 ‐0.10

120.1 Archstone Fox Plaza San Francisco, CA Residential 0.65 0.13 0.52

122.1 Bong Su San Francisco, CA Restaurant 0.64 0.18 0.47

142.1 Berkeleyan Apartments Berkeley, CA Residential 0.27 0.18 0.08

144.2 Acton Courtyard Berkeley, CA Restaurant 0.74 0.04 0.70

201.2 343 Sansome San Francisco, CA Coffee 0.32 0.23 0.09

215.1 Broadway Grand Oakland, CA Residential 0.71 0.71 0.00

220 2 Park To er Sacramento CA Coffee 0 34 0 40 0 07

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220.2 Park Tower Sacramento, CA Coffee 0.34 0.40 ‐0.07

222.2 Convention Plaza San Francisco, CA Coffee 0.33 0.29 0.04

PM Model Validation

• PM Model: 7 of 14 model predictions within 50% of observed

ID Site Name City

General LU

Category PM Model 1 Output

Observed PM

Actual/ITE

PM Model 1‐

Observed

113.1 Central City Association of Los Angeles Los Angeles, CA Office 0.28 0.32 ‐0.05

114.1 Horizon San Diego, CA Residential 0.59 0.35 0.24

115.1 Atria San Diego, CA Residential 0.58 0.62 ‐0.03

120.1 Archstone Fox Plaza San Francisco, CA Residential 0.50 0.16 0.34

122.1 Bong Su San Francisco, CA Restaurant 0.49 0.62 ‐0.13

142.1 Berkeleyan Apartments Berkeley, CA Residential 0.43 0.18 0.25

143.1 Touriel Building Berkeley, CA Residential 0.42 0.30 0.12

144.2 Acton Courtyard Berkeley, CA Restaurant 0.61 0.23 0.38

146.1 Bachenheimer Building Berkeley, CA Residential 0.42 0.08 0.34

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203.2 Fruitvale Station Oakland, CA Coffee Shop 0.33 2.20 ‐1.87

208.1 Paseo Colorado Pasadena, CA Retail 0.60 0.41 0.19

215.1 Broadway Grand Oakland, CA Residential 0.57 0.52 0.05

220.2 Park Tower Sacramento, CA Coffee Shop 0.22 0.28 ‐0.06

222.2 Convention Plaza San Francisco, CA Coffee Shop 0.22 0.33 ‐0.12

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AM Model Validation

2.50

AM Model Output vs. Observed Values

1.00

1.50

2.00

bserved Values (Actual/ITE)

Line shows where model = observed

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0.00

0.50

0.00 0.50 1.00 1.50 2.00 2.50

Ob

Model Values (Actual/ITE)

PM Model Validation

2.50

PM Model Output vs. Observed Values

Busy PM coffee shop in auto‐oriented shopping complex

1.00

1.50

2.00

bserved Values (Actual/ITE)

Line shows where model = observed

oriented shopping complex

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0.00

0.50

0.00 0.50 1.00 1.50 2.00 2.50

Ob

Model Values (Actual/ITE)

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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 g p p y

in final model• Final model balance theory and practice.

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Conclusions and Implications

• This study:

1 Provides additional evidence that ITE Trip1. Provides additional evidence that ITE Trip Generation does not address smart growth contexts.

2. Works to complement the existing ITE Trip Generationmethod.

3. Provides a method for practitioners to apply basic rate adjustments at smart growth sites.

4. Creates the foundation for multimodal person‐trip p pdatabase.

5. Informs national trip generation practice 6. Is being proposed as part of a revision to the

ITE Trip Generation Handbook.

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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 CollectionData Collection– Ewald & Wasserman Research Consultants– Gene Bregman & Associates– Manpower, Inc.

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Image source: Benjamin Sperry

Acknowledgements

• Other: Existing methodology review, Data Management QA/QCData 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

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Image source: Benjamin Sperry

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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– Samir Haijiri, City of San Diego– Pang Ho, PH Associates– Don Hubbard, Parsons Brinckerhoff– Eric Ruehr, VRPA Technologies, Inc.– Edward Sullivan, Economic & Planning Systems

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Questions & Discussion

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For more information, see the project website:

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

Documents

California Smart Trip Generation Rates Studydownloads.ice.ucdavis.edu/ultrans/smartgrowthtripgen/ITS-Davis... · Trip Generation Rates Study ... 13.1 Holly Street Village 151 E. Holly