I nternal Capture in Mixed-Use Developm ents ( MXDs) and Vehicle - - PowerPoint PPT Presentation

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I nternal Capture in Mixed-Use Developm ents ( MXDs) and Vehicle Trip and Parking Reductions in Transit-Oriented Developm ents ( TODs)

Department of City & Metropolitan Planning, University of Utah

Reid Ewing

City and Metropolitan Planning University of Utah ewing@arch.utah.edu

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The Ds of the Built Environment

• Density
• Diversity
• Design
• Destination Accessibility
• Distance to Transit
• Development Scale
• Demand Management

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I nternal Capture in MXDs

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Tian, G., Ewing, R., White, A., Hamidi, S., Walters, J., Goates, J. P., & Joyce, A. (2015). Traffic Generated by Mixed-Use Developments: Thirteen- Region Study Using Consistent Measures of Built Environment. Transportation Research Record: Journal of the Transportation Research Board, (2500), 116-124. Ewing, R., Greenwald, M., Zhang, M., Walters, J., Feldman, M., Cervero, R., & Thomas, J. (2010). Traffic generated by mixed-use developments— Six-region study using consistent built environmental measures. Journal

• f Urban Planning and Development, 137(3), 248-261.

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Introduction

Mixed-use development (MXD)

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 Put offices, shops, restaurants, residences, and

• ther codependent activities

in close proximity to each

• ther;

 Shorting trips and allow what might otherwise be external car trips to become internal walk, bike, or transit trips;  Reduce the vehicle miles;

RiverPlace, Portland: residential, commercial, dining, medical, salon, entertainment

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Conventional traffic impact analysis

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ITE method for MXDs 1) Determine the amounts of different land-use types (residential, retail, and office) contained within the development; 2) preliminary estimation = Land-use types * ITE’s per- unit trip generation rates (no interactions); 3) Reduced estimation = preliminary estimation - a certain percentage to account for internal-capture of trips within MXDs. The reductions are based on lookup tables; 4) For each pair of land uses, productions and attractions of internal trips are reconciled

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Weaknesses of the current method 1) The two lookup tables are based on data for a “limited number of multiuse sites in Florida” ; 2) The land-use types and adjustments embodied in the lookup tables are limited to the three uses: residential, retail, and offices; 3) The scale of development is disregarded; 4) The land-use context of development projects is ignored; 5) The possibility of mode shifts for well-integrated, transit-served sites is not explicitly considered;

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 Accurately estimating the proportion of trips captured internally by MXDs is vitally important to accurately assess MXD projects’ traffic impacts.  Top down: assemble enough data on MXDs to estimate statistical models of traffic generation in terms

• f standard built environmental variables;

Taking this approach, Ewing et al. (2010) modeled internal capture rates and external walk and transit mode shares with data for more than 35,000 trips to/from/within 239 MXDs in six metropolitan regions of the U.S.

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This paper builds on the earlier work of Ewing et al. (2010) with more and newer data.

Regions MXDs Trips Earlier study 6 239 35,877 Current study 13 412 70,074

 We now have enough regions in our database so that regional variables may prove statistically significant in a multi-level

• analysis. This means that, for the first time, traffic analysts can

tailor their analyses to the unique characteristics of their home regions.  We now have enough bike trips in our database to model the probability that an external trip will be by bicycle.

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Conceptual framework

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Sample selection

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• Regional household survey with XY coordinates;
• Parcel level land-use data;

Household travel survey

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Gateway district, Salt Lake City: dining, entertainment, retail, residential, office

• A mixed-use development or

district consists of two or more land uses between which trips can be made using local streets, without having to use major streets. The uses may include residential, retail, office, and/or entertainment. There may be walk trips between the uses.

• Expert-based process

MXDs selection

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Survey year MXDs Mean acreage per MXD Total trip ends Mean trip ends per MXD Atlanta 2011 49 123 2,574 53 Austin 2005 42 206 1,504 36 Boston 2011 39 52 9,995 263 Denver 2010 25 123 3,381 147 Eugene 2009 4 93 2,931 733 Houston 2008 48 445 3,929 91 Kansas City 2004 16 113 1,280 80 Minneapolis-St. Paul 2010 36 124 8,469 235 Portland 2011 46 119 6,252 130 Sacramento 2000 25 179 2,487 99 Salt Lake City 2012 19 110 2,354 124 San Antonio 2007 5 46 2,62 87 Seattle 2006 58 233 21,063 370 Total 412 192 66,481 161

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Variables

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Outcome variables Definition INTERNAL Dummy variable indicating that a trip remains internal to the MXD (1 = internal, 0 = external). WALK Dummy variable indicating that the travel mode on an external trip is walking (1 = walk, 0 = other). BIKE Dummy variable indicating that the travel mode on an external trip is biking (1 = bike, 0 = other). TRANSIT Dummy variable indicating that the travel mode on an external trip is public bus or rail (1 = transit, 0 = other). Explanatory variables Level 1 traveler/household level HHSIZE Number of members of the household. VEHCAP Number of motorized vehicles per person in the household. BUSSTOP Dummy variable indicating that the household lives within 1/4 mile of a bus stop (1 = yes, 0 = no)

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Explanatory variables Level 2 MXD explanatory variables AREA

Gross land area of the MXD in square miles.

POP

Resident population within the MXD; prorated sum of the population for the census block groups that intersect the

• MXD. Prorating was done by calculating density of population per residential acre (tax lots designated single-family or

multifamily) for the entire census block group, then multiplying the density by the amount of residential acreage within the block group contributing to the MXD, and finally, summing over all block groups intersecting the MXD area. For Houston, data at the traffic analysis zone (TAZ) level were prorated.

EMP

Employment within the MXD; weighted sum of the employment within the MXD for all Standard Industrial Classification (SIC) industries. For Portland, employment estimates were based on the average number of employees in each size category, summed across employer size categories. For other regions, data at the TAZ level were prorated.

ACTIVITY

Resident population plus employment within the MXD.

ACTDEN

Activity density per square mile within the MXD. Sum of population and employment within the MXD, divided by gross land area.

DEVLAND

Proportion of developed land within the MXD.

JOBPOP

Index that measures balance between employment and resident population within MXD. Index ranges from 0, where

• nly jobs or residents are present in an MXD, not both, to 1 where the ratio of jobs to residents is optimal from the

standpoint of trip generation. Values are intermediate when MXDs have both jobs and residents, but one predominates.

a

LANDMIX

Another diversity index that captures the variety of land uses within the MXD. This is an entropy calculation based on net acreage in land-use categories likely to exchange trips b. The entropy index varies in value from 0, where all developed land is in one of these categories, to 1, where developed land is evenly divided among these categories.

INTDEN

Number of intersections per square mile of gross land area within the MXD.

EMPMILE

Total employment outside the MXD within one mile of the boundary. Weighted average for all TAZs intersecting the

• MXD. Weighting was done by proportion of each TAZ within the MXD boundary relative to an entire TAZ area (i.e.,

“clipping” the block group with the MXD polygon).

EMP30T

Percentage of total regional employment accessible within 30-min travel time of the MXD using transit.

EMP10A, EMP20A, EMP30A

Percentage of total regional employment accessible within 10, 20, and 30-min travel time of the MXD using an automobile at midday.

STOPDEN

Number of transit stops within the MXD per square mile of land area. Uses 25 ft buffer to catch bus stops on periphery.

RAILSTOP

Rail station located within the MXD (1 = yes, 0 = no). Commuter, metro, and light rail systems are all considered.

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Explanatory variables Level 3 regional explanatory variables REGPOP Population within the region. INDEX Measure of regional compactness developed by Ewing and Hamidi (Ewing and Hamidi, 2014). Index derived by extracting the common variance from multiple measures through principal component analysis.

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Region Internal capture (percentage of all trips) Mode share percentage for external trips Walk share Bike share Transit share Sum of walk, bike and transit Atlanta 16.4% 4.1% 1.1% 2.6% 7.8% Austin 16.8% 1.4% 1.8% 0.2% 3.4% Boston 21.0% 36.7% 2.2% 28.3% 67.2% Denver 26.5% 7.2% 1.5% 6.9% 15.5% Eugene 24.8% 9.6% 5.0% 13.2% 27.8% Houston 14.8% 1.2% 0.4% 1.5% 3.1% Kansas City 11.1% 2.6% 0.9% 2.9% 6.4% Minneapolis-St. Paul 18.7% 10.7% 2.4% 12.4% 25.4% Portland 25.6% 13.6% 4.5% 12.4% 30.5% Sacramento 16.4% 2.9% 0.4% 0.4% 3.8% Salt Lake City 11.6% 7.4% 1.4% 4.2% 13.0% San Antonio 4.6% 2.4% 0.8% 6.0% 9.2% Seattle 20.2% 7.8% 2.0% 10.0% 19.8% Overall 19.7% 11.9% 2.1% 11.3% 24.3%

Average internal capture rates and walk, bike and transit mode shares for external trips to/from MXDs

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Models

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Level 3 Regions Level 2 MXDs Level 1 Trips/Households  Trip purpose: home-based work, home- based other, non-home-based;  Dichotomous outcomes: internal versus external, walk versus other, bike versus other, and transit versus other;  Hierarchical modelling: trips/households are nested within MXDs; MXDs are nested within regions;  Random intercept models: only the intercepts were allowed to randomly vary across higher level units.

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Results

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Table 1. Log odds of internal capture (log-log form)

Home-based work Home-based other Non-home based Coef. t-ratio p-value Coef. t-ratio p-value Coef. t-ratio p-value Constant 9.981

• 3.288
• 6.564

REGPOP

• 1.028
• 3.29

0.008 — — — — — — INDEX — — — — — — 1.018 4.238 < 0.001 AREA 0.667 2.734 0.007 0.914 4.163 < 0.001 0.242 3.462 0.001 EMP — — — — — — 0.155 3.952 0.001 JOBPOP 0.737 4.099 < 0.001 0.587 6.063 < 0.001 — — — INTDEN 0.968 3.066 0.003 0.711 2.365 0.019 — — — HHSIZE

• 1.108
• 8.01 < 0.001
• 0.766
• 3.796 < 0.001
• 0.339 -7.118 < 0.001

VEHCAP

• 2.479
• 5242 < 0.001
• 1.563
• 6.292 < 0.001
• 0.597 -7.391 < 0.001

Pseudo-R2 0.05 0.24 0.25

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Table 2. Log odds of walking on external trips (log-log form)

Home-based work Home-based other Non-home-based Coefficient t-ratio p-value Coefficient t-ratio p-value Coefficient t-ratio p-value Constant

• 8.739
• 9.338
• 11.413

AREA

• 0.249
• 3.016

0.003

• 0.263
• 2.404

0.017

• 0.220
• 2.624

0.009 ACTDEN — — — 0.590 5.900 < 0.001 0.850 11.090 < 0.001 JOBPOP — — — 0.231 3.371 0.001 — — — LANDMIX 1.266 2.242 0.026 — — — — — — INTDEN 0.462 3.051 0.003 0.331 2.039 0.042 — — — EMPMILE 0.339 5.686 < 0.001 — — — — — — EMP10A — — — — — — 0.188 2.959 0.004 HHSIZE

• 1.438
• 5.956 < 0.001
• 0.644
• 7.360 < 0.001
• 0.192
• 2.075

0.038 VEHCAP

• 2.075
• 4.024 < 0.001
• 2.131
• 5.073 < 0.001
• 0.813
• 3.209

0.002 BUSSTOP 1.091 3.188 0.002 0.631 3.962 < 0.001 — — — Pseudo-R2 0.43 0.58 0.56

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Table 3. Log odds of biking on external trips (log-log form)

Home-based work Home-based other Non-home-based Coefficient t-ratio p-value Coefficient t-ratio p-value Coefficient t-ratio p-value Constant

• 20.175

1.930

• 0.302

REGPOP — — —

• 0.751
• 4.892 < 0.001
• 0.632
• 2.446

0.033 INDEX 2.180 1.956 0.076 — — — — — — AREA

• 0.149
• 2.481

0.014 — — —

• 0.342
• 2.488

0.014 ACTDEN 0.465 3.301 0.001 — — — — — — JOBPOP — — — 0.326 3.429 0.001 — — — INTDEN — — — 0.401 1.909 0.057 — — — EMPMILE — — — 0.422 4.590 < 0.001 0.479 4.191 < 0.001 STOPDEN — — — — — —

• 0.217
• 2.502 < 0.013

EMP30T 0.367 2.275 0.023 — — — — — — HHSIZE 0.385 2.378 0.018 — — — — — — VEHCAP

• 1.593
• 3.329

0.001

• 1.701
• 3.701 < 0.001
• 0.931
• 2.238

0.025 BUSSTOP 1.186 5.397 < 0.001 — — — 0.857 2.290 0.022 Pseudo-R2 0.42 0.31 0.16

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Table 4. Log odds of transit on external trips (log-log form)

Home-based work Home-based other Non-home-based Coefficient t-ratio p-value Coefficient t-ratio p-value Coefficient t-ratio p-value Constant

• 11.205
• 11.386
• 6.628

ACTDEN 0.824 10.400 < 0.001 0.782 5.019 < 0.001 — — — EMPMILE — — — — — — 0.221 3.878 < 0.001 JOBPOP 0.143 2.750 0.007 — — — — — — STOPDEN — — — — — — 0.613 5.779 < 0.001 HHSIZE

• 0.791
• 2.591

0.010

• 0.665 -16.375 < 0.001
• 0.522
• 3.414

0.001 VEHCAP

• 3.291
• 5.698 < 0.001
• 3.938
• 6.353 < 0.001
• 1.754
• 3.274

0.001 BUSSTOP 1.054 2.540 0.011 0.595 2.834 0.005 — — — Pseudo-R2 NA NA 0.43

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Conclusion

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 Failure to account for internal capture and external walk, bike, and transit trips ends up penalizing MXDs, ITE’s trip generation overestimates of the traffic impacts of MXD projects;  This research sought to advance the state of knowledge

• n the relationships that govern travel to, from, and

within mixed-use development projects and to enumerate tangible and verifiable traffic reductions relative to the rates in the ITE Trip Generation report.  This study represents the first large-sample national study of the traffic generation by mixed-use developments, making use of household travel survey data from 13 metropolitan regions.

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Mode Shares and Vehicle Trip and Parking Generation at TODs

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Tian, G., Ewing, R., Weinberger, R., Shively, K., Stinger, P., & Hamidi, S. (2016). Trip and parking generation at transit-oriented developments: a case study of Redmond TOD, Seattle region. Transportation, 1-20. Ewing, R., Tian, G., Lyons, T., Weinberger, R., Shively, K., & Stinger, P. (2016) Trip and parking generation at transit-oriented developments. National Institute for Transportation and Communities (NITC) report.

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Introduction

How best to allocate land around transit stations?

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large park-and- ride lots

Redmond TOD, Seattle

active uses such as multifamily housing,

• ffice, and retail

VS.

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 Officials usually assuming that TODs generate the same number of vehicle trips as conventional development and that transit stations require the same number of park-and-ride spaces as non-TOD stations.

In practice

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The average trip generation rate in areas with TOD is well below the trip generation rate from the ITE report (Arrington & Cervero 2008; Cervero &

Arrington 2008; Cervero et al. 2004).

Residents living within TODs are reported to have higher rates of transit trips than who are living outside TOD (SFBAMTC 2006; Cervero et al.

2002; Faghri & Venigalla 2013; Zamir et al. 2014), especially for commuting trips

(Arrington & Cervero 2008; Cervero 1994; Lund et al. 2004; Lund et al. 2006).

In literature

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Much of the travel demand is captured internally and much of the transit demand is generated by TODs themselves. Transit trips Vehicle trips Internal trips

There are a few studies of vehicle trip generation (Arrington & Cervero, 2008; Cervero &

Arrington, 2008; Zamir et al. 2014) at multifamily developments near transit. There is

• nly one study of vehicle trip generation at TODs (defined as mixed-use

developments – Handy et al. 2013). The question of how much vehicle trip reduction occurs with TOD is largely unexplored in the literature.

Research Question

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TOD Definition

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TODs are widely defined as compact, mixed-use developments with high-quality walking environments near transit facilities (ITE 2004, pp. 5-

7; Jacobson & Forsyth 2008; Renne 2009).

For our purposes, TODs are developed by a single developer under a master development plan, and can also include a clustering of development projects near transit facilities that are developed by one

• r more developers pursuant to a master development plan.

Dense Built after transit Mixed use Fully developed

• r nearly so

Pedestrian- friendly Self-contained parking Adjacent to transit

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TOD Selection

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Mixed use developments (MXDs) near transit Regional transit agencies and MPOs Google Satellite Imagery Site visit

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Redmond TOD, Seattle Rhode Island Row, Washington D.C. Fruitvale Village, San Francisco Englewood TOD, Denver Wilshire/Vermont, Los Angeles

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Table 1. Net and Gross Residential Densities, and Floor Area Ratios for Commercial Uses, for the Five TODs Studied

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Data Collection

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 A full count of all persons entering and exiting the building  A brief intercept survey of a sample of individuals entering and exiting the building

• “How did you get here?” (e.g., by

what mode of travel?), and

• What is the purpose of your trip?

7:30 a.m. and 9:00 p.m. on a workday in spring or fall 2015

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Table 2. Average Mode Shares for TODs Studied Table 3. Average Vehicle Trip Reductions Relative to ITE Rates

Mode Choice and Trip Generation

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Figure 2. Vehicle Trip Generation vs. Auto Mode Share

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Table 5. Residential Parking Supplies as a Percentage of ITE, and Residential Peak Parking Demand as a Percentage of Actual Supplies

Parking Supply and Demand

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Table 6. Commercial/Mixed Use Parking Supplies as a Percentage of ITE, and Commercial/Mixed Use Peak Parking Demand as a Percentage of Actual Supplies Table 7. Aggregate Parking Supplies as a Percentage of ITE Supplies, and Aggregate Peak Parking Demand as a Percentage of Actual Supplies

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Conclusion

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 Walk mode shares fall within a fairly narrow band, from 16.6 % at Rhode Island Row to 28.3 % at Fruitvale. They mostly reflect the environment in which the TOD is located, and secondarily the number of commercial trip attractions contained within the TOD;  The smallest rail mode share is 13.6 % at Englewood. The largest shares are 27.2 % at Rhode Island Row and 26.1 % at Fruitvale;  The number of vehicle trips at TODs range from one- third below to two-thirds below ITE rates;

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 The supply of parking stalls for residential use at TODs ranges from 0.81 to 1.60 stalls per dwelling unit; the peak demand for parking ranges from 0.44 to 1.29 spaces per

• ccupied dwelling ; the occupancy of residential parking

spaces ranges from 54.3% to 80.6 %;  Actual parking supplies for commercial and mixed-use garages and lots in our TODs range from 22.6% to 61.2%

• f ITE supplies at Englewood;

 At the overall peak hour, parked cars would fill only 19.0% to 45.8% of parking spaces if built to ITE standards and only 58.3% to 84.0% of parking spaces will be filled.

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Thank you !