Residential Parking Working Group Meeting Ten Read-Ahead … · Capital Bikeshare station in lieu...

Residential Parking Working Group Meeting Ten Read-Ahead Materials of 29

RESIDENTIAL PARKING WORKING GROUP MEETING TEN READ-AHEAD MATERIALS In preparation for Residential Parking Working Group Meeting Ten, staff has assembled answers to a

variety of questions that we received at the last Working Group meeting on February 6th, 2017.

Question or Request Short Answer

Does Denver allow car sharing spaces within the 1,500 ft. limit to be located on street, or is it only off street?

No; only off-street car sharing spaces may be counted.

How much does 10 bike parking spaces cost or what is a unit cost for bike parking that we can compare to vehicle parking cost?

Staff does not have cost estimates at this time. We ask that any members of the Working Group with knowledge of these costs to bring information.

What are Arlington’s bike parking location standards?

We have included the basic standards for secure, long-term bike parking.

Do we have an idea of the cost of a developer-paid revenue-guarantee for car sharing?

No; we ask that representatives enquire about this with colleagues. However, regulations in Vancouver, Canada may provide guidance on how to “price” a car sharing service guarantee.

Please provide map of Capital Bikeshare expansion plans in Arlington

Staff have provided a map. However, note that the County is willing to accept the installation of a station at any location provided that the developer:

Locates it on private property with public access.

Pays for the capital cost.

Pays for operating expenses.

What would staff propose as a vehicle-parking reduction in exchange for investment in Capital Bikeshare stations?

A reduction of 2 vehicle parking spaces for one 11-dock station with a sponsorship agreement, 3 for a 15-dock station, 4 for 19-dock station.

What were the trip-performance standards in the Mazda Site [SP440] approval?

Developer agreed to monitor grocery store trips for 2 years. If the share of trips is above 50%, then mitigation measures will be required.

Please provide unweighted averages of parking demand by transit areas.

Instead of simply providing the unweighted figures, we have provided a description of the entire data-analysis process used to arrive at the numbers shown at the February 6th meeting.

Complete answers can be found on the following pages.

We have also prepared a schedule for the remainder of the Working Group process (dates subject to

change).


Schedule for the remainder of the Working Group process (subject to change) Month Day Event/Deadline

February 22 Working Group Meeting Ten

28 Final Working Group Meeting

March

2 Presentation to CMO on draft Working Group recommendation prior to public engagement

6-31 Public Engagement: (Goals: discover any missed considerations; record issues with the Working Group’s

proposal and the magnitude of feeling about the issue for consideration as staff develops proposal).

a. Publicize through newsletters (Arlington County Government and ATP) b. Online comment c. Receive e-mails to general e-mail address d. Present to Arlington Ridge, Aurora Highlands, and Ashton Heights Civic Associations (if requested) e. Engage smart growth advocates f. Online webinars to reach out to BIDs, NAIOP, and affordable housing developers, as well as others

10 (ideally) Working Group finishes report

April

3 Info item at Transportation Commission

4 Presentation to Civic Federation (James Schroll unavailable in March)

11 Info item at Economic Development Commission

13 Info item at Housing Commission

17 (week of) Complete DES, CPHD, and AED review and approve staff recommendation (starts in February)

24 (week of) Brief Mark Schwartz and make final modifications to the staff recommendation

May …

June 1 Action item at Transportation Commission

5 or 7 “…” at Planning Commission

8 “…” at Housing Commission

12 (week of) Board Briefings

13 Action item at Economic Development Commission

17 or 20 Board Action


Does Denver allow car sharing spaces within the 1,500 ft. limit to be located on street, or is it only off street? No; only off-street car sharing spaces may be counted. The following conditions are part of Article 10,

Section 4.5.3 B (page 10.4-11):

The car sharing program shall be: (1) Located on a zone lot and not within public right-of-way;

(2) Located within a walking distance no greater than 1,500’ of the zone lot containing the

subject primary use;

(3) Either in existence or being developed concurrently with the proposed development;

and

(4) Has the capacity to meet a portion of the vehicle parking needs of the subject primary use.

Also, note that while on-site car sharing can be provided at a ratio of 5 required vehicle parking spaces

reduced for each 1 car share space provided, that reduction is “[d]etermined by the Zoning

Administrator” if the car sharing service is provided off site (p. 10.4-11).

How much do 10 bike parking spaces cost or what is a unit cost for bike parking that we can compare to vehicle parking cost? Staff does not have good data on the cost of building secure bike parking. We ask that anyone on the

Working Group who has estimates to bring them for the Working Group’s consideration. Keep in mind

that for underground bike parking, the total cost is cost of constructing an underground space and then

installing a secure bike cage or room as well as bike racks; the total cost of constructing an above ground

bike room is simply the cost of constructing any room with a locked door and then installing bike racks.

What are Arlington’s bike parking location standards? Arlington County has standards for two kinds of bike parking: short-term visitor parking (class III) and

long-term secure parking (class I). As the Working Group has been discussing long-term secure parking,

staff have summarized the standards for that parking here.

Class I parking must provide protection from weather; security of locked room or cage; ability to

lock bicycle to a rack within the room or cage.

Location

Best: Fully enclosed ground floor room with sidewalk access

Second Best: Fully enclosed room in garage

Third Best: Cage in a garage

In Special Circumstances: Bike lockers

Room Design Requirements

https://www.denvergov.org/content/dam/denvergov/Portals/646/documents/Zoning/DZC/Denver_Zoning_Code_Article10_Design_Standards.pdf


At least 30% of bicycle parking must be horizontal and at ground level

Cage walls must be made of industrial grade expanded metal or welded wire mesh

Doors must be hollow metal

Doors must use a heavy-duty cipher lock or electronic lock

Cages only: door handles and locks supplemented by sheet metal plating, 3 feet in each

direction, as needed to prevent tampering

Cage walls must reach all the way to ceiling

For more information, see www.commuterpage.com/bikeparkingstandards.

Do we have an idea of the cost of a developer-paid revenue-guarantee for car sharing? Staff does not have data to answer this question and believe that the number will vary not only by

region but also by location within the region. This question could be answered by Working Group

members or observers who are in the real-estate development community and who have worked with

car sharing providers to offer service on site at residential projects.

One point of reference comes from the City of Vancouver, Canada. There, developers are required to

agree to a letter of credit at a rate of $50,000 per year for 3 years. If the building owner does not ensure

that car sharing service is provided, the City may call on that letter of credit from the developer and use

the funds to make sure the service is provided. Any funds in excess of what the City needs to ensure that

service is provided can be refunded to the building owner.

Provide map of Capital Bikeshare expansion plans in Arlington The cost of a Capital Bikeshare station has two elements: the one-time cost of installation and on-going

operation costs to cover maintenance and other services. Arlington County has an existing sponsorship

program by which private entities can pay for the cost of station installation and operating expenses.

The private entity must also provide space for the station on private property, as some locations do not

have space in the public right of way. Currently, the cost to install a Capital Bikeshare station follows a

schedule based on the number of docks.

Approximate Capital Bikeshare Station Sponsorship Costs

Station Size Equipment and Installation Cost (approx.)

Operating Costs* Monthly (yearly)

Station with bikes and 11 docks $40,000 $1,200 ($14,400)



*Operating costs subject to increases up to 5% each year.

Currently there is no minimum time period over which a sponsor must pay the operating costs, though

staff would likely consider a minimum time period as part of a parking-policy recommendation. Staff

http://www.commuterpage.com/bikeparkingstandards


does not support arrangements that would pay for capital costs alone, as this would create an operating

cost for the County that would not have been budgeted. Staff would not support the sponsorship of a

Capital Bikeshare station in lieu of secure, on-site bike parking. Note, however, that the County will

accept sponsorship agreements for any location as long as space for the station is provided, as well as

capital and operating costs.

This map depicts current expansion plans for the Capital Bikeshare system in Arlington.

Arlington Capital Bikeshare Expansion Map

Source: http://www.bikearlington.com/pages/bikesharing/planned-arlington-stations/

What would staff propose as a vehicle-parking reduction in exchange for investment in Capital Bikeshare stations? For consistency with the off-street, secure bike-parking proposal presented at the February 6th meeting,

staff propose that sponsorship for one 11-dock station would be accepted for a reduction of 2 parking

spaces; a 15-dock station would be accepted for a reduction of 3 parking spaces, and a 19-dock station

would be accepted for a 4-space reduction.

Locations with green

dots are potential future

sites

Stations planned for

installation in FY2017,

FY2018, and FY2019 are

marked with their year

of planned expansion

http://www.bikearlington.com/pages/bikesharing/planned-arlington-stations/


As a point of reference, the City of Denver also allows reductions of one space for every five bike share

docks.

What were the trip-performance standards in the Mazda Site [SP440] approval? The following text is from page 21 and 22 of the staff report for the project.

Transportation Demand Management (TDM):

The applicant has agreed to implement a standard Transportation Management Plan (TMP) to

provide a program oriented towards decreasing single-occupancy vehicle (SOV) trips to and

from the site. The TMP program includes the elements summarized below:

Designation of a transportation coordinator to oversee the TMP program.

Contribute to Arlington County Commuter Services (ACCS) to support TMP activities.

Provision of a $65 SmarTrip® card, or a one-year Capital Bikeshare membership, or a

one-year carshare membership to each new residential lessee and on-site management

employee, at initial occupancy.

Display and distribute transit-related information.

Reimburse the County for and participate in periodic transportation performance

monitoring studies, to determine average vehicle occupancy and mode choice.

Submission of an annual report to the County regarding TDM activities on the site.

In addition, the applicant has agreed to enhance its TDM program to attempt to further reduce

the number of SOV trips at the site, and to offer transportation alternatives to residents,

employees, and shoppers using the site. The “enhanced” TDM measures include: hiring a TDM

coordinator specifically for the grocery store; providing information and promotional signage for

the availability of carshare, and rideshare or taxi designated pick-up areas; providing real-time

transportation informational displays visible from within and outside the grocery store, at

building entrances on N. Glebe Road and N. Tazewell Street; providing carshare parking spaces

within the garage; providing additional incentives to grocery store employees to use transit or

alternative transportation modes; and providing a limited number of free promotional wire

pullcarts for grocery store patrons. Additionally, the applicant agrees to monitor grocery store

trips for two years, to ensure that the vehicular driving rate remains below 50% of trips.

Mitigation measures, including additional services, programs, and other initiatives to reduce

drive-alone trips will be put in place should the driving mode share exceed the 50% rate.

[emphasis added]

This 50% threshold is related to assumptions made for a traffic impact analysis submitted by the

developer in April 2015. From the same staff report (page 15):


Trip Generation: Gorove/Slade Associates, Inc. prepared a traffic impact analysis (TIA) dated

May 26, 20151, assuming a development plan for the site that includes 483 residential units, a

43,000 square-foot grocery store, and an additional 24,000 square feet of ground floor retail

use. Upon site build-out (projected for 2018) the project is estimated to generate 215 morning,

361 evening, and 402 Saturday peak-hour vehicle trips.

The trip generation assumptions for this site were agreed to at the original scoping for the TIA in

April 2015. One of those assumptions was that the drive-alone mode share for trips at this site

would be reduced by 50% from the Institute of Transportation Engineers (ITE) standard. That

assumption is based upon the urban location of the site, its high walk and bike scores, its

proximity to high-speed and high-frequency transit, the large number of residents living within a

short distance from the site, both current and projected, the large number of employees

working within a short distance of the site, and the observed mode share of similar types of

mixed-use grocery store developments in Arlington and Washington. An analysis2 of five mixed-

use development grocery stores in close proximity to Metro (< 0.5 mile) with high walk and

transit scores and proximity to major arterials showed that the number of vehicular trips is 15%

to 35% of the number of trips that would be expected using the ITE standard. One mixed-use

grocery store development with high transit and high walk scores, but further (1.7 miles) from

Metro, had a vehicular trip generation rate of 39% (am peak) to 67% (pm peak) of the ITE

standard. For this site, Gorove/Slade estimates that vehicular traffic generated by this site would

be 44% to 45% of ITE’s estimates. Thus, the 50% mode share reduction has been assumed and

incorporated into the subsequent analyses.

Provide unweighted averages of parking demand by transit areas At Working Group Meeting Nine, staff presented an overview of the analysis of recent parking-demand

data that they undertook in order to inform proposals for new parking minimums related to transit

access. Recall that recent parking demand is only one way to set parking supply requirements for the

future.

For analysis, staff consulted four sources of data that describe parking demand in the recent past in

Arlington:

1. Counts of vehicles at garages in residential Site Plan buildings as gathered through periodic

monitoring studies that are required of many Site Plan developments.

2. Vehicle-registration data from the Commissioner of Revenue expressed as the number of

vehicles per unit of housing registered to addresses at multi-family buildings in Arlington.

3. Parking-space leases at rental apartments in the two Metro Corridors as collected by Neal

Kumar of Rushmark Properties and the Arlington Economic Development Commission.

4. A 2015 survey of Arlington residents fielded by Arlington County Commuter Services.

1 Updated September 11, 2015, with a supplemental technical memorandum issued on January 21, 2016. 2 “Observed Trip Generation in Mixed-Use Developments with Grocer: Washington, DC and Arlington, VA”,

Gorove/Slade, March 4, 2016


The following table numerically describes these data sets and also provides comment on how each data

set might be biased toward greater numbers (marked with a “+” sign) or biased toward fewer numbers

(“-“ sign) simply because of the way each data set was collected.


Characteristics of Various Parking Demand Data Sources for Multi-Family Buildings in the Rosslyn-Ballston and Jefferson Davis Corridors

Data from Neal Kumar Commissioner of Revenue Data

Resident Study 2015

Performance Monitoring Studies

Unit of Measure Parking Lease/Unit

Parking Lease/Occupied Unit

Registered Vehicles/Unit

Vehicles/ Household

Vehicles/Unit

Highest Quartile 1.51 1.59 1.24 1 1.3

75 Percentile 1.07 1.13 0.95 1 1.07

Mean 0.98 1.03 0.84 1.02 0.93

Median 0.93 0.98 0.81 1 0.89

25 Percentile 0.78 0.82 0.68 1 0.8

Lowest Quartile 0.49 0.52 0.33 1 0.64

Characteristics of Data

Sample Size 49 bldgs 49 bldgs 97 bldgs 976 individuals 22 bldgs

Includes On and Off-Street? No (-) No (-) Yes (+) Yes (+) No (-)

Includes Visitor Parking? No (-) No (-) No (-) No (-) Yes (+)

Captures Un-Registered Vehicles?

Yes (+) Yes (+) No (-) Yes (+) Yes (+)

Occupied Units? No (-) Yes (+) No (-) Yes (+) No (-)

Subject to Seasonal Parking Demand?

No No No No Yes

Other Notes Potential sample bias

The box and whisker plot on the next page depicts the same data sets graphically. In this plot, the box represents the range between the 25th and 75th percentile values. The “x” marks represent the mean, while the line inside of the box is the median (or 50th percentile value). The thin lines or “whiskers” extend to the 0th percentile and 100th percentile; dots represent “outliers.”


Staff then looked at the relationships between the distance to Metro and the maximum observed parking demand (in the case of the Site Plan

studies) or the number of vehicles registered per unit (in the Commissioner of Revenue data).

y = 0.7254x + 0.7401R² = 0.2495

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Maximum Parking Demand by Distance from Metro Stations, Site Plan Buildings

Less than One Mile, Unbundled Less than One Mile, Bundled

More than One Mile from Metro, Unbundled More than One Mile from Metro, Bundled

Linear (Less than One Mile, Unbundled)


As seen in the chart above, analysis of the Site Plan data set revealed that a statistically significant relationship between parking demand and

distance to Metro is only present for buildings closer than 1 mile to a Metro station (in this case, as measured by walking distance, not “straight-

line” or “crow-flies” distance). The relationship was stronger at properties that “unbundle” parking charges. The following linear regression

output describes the relationship between the independent variable (distance to Metro) and the dependent variable (maximum observed

parking demand) for those 17 buildings with unbundled parking in the data set.

REGRESSION SUMMARY OUTPUT FOR PROPERTIES LESS THAN 1 MILE FROM METRO, WITH UNBUNDLED PARKING

Regression Statistics Multiple R 0.499513505

R Square 0.249513742 Adjusted R Square 0.199481325 Standard Error 0.173552359 Observations 17

ANOVA df SS MS F Significance F

Regression 1 0.150211791 0.150211791 4.987041517 0.041197329 Residual 15 0.451806318 0.030120421

Total 16 0.602018109

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 0.740142404 0.093003454 7.958224926 9.1839E-07 0.541910235 0.938374573

Distance to Station (miles) 0.7254494 0.324852067 2.233168493 0.041197329 0.03304361 1.417855191

Note: Three buildings were not included due to a lack of parking-demand data.

The Significance F value is significant at the 95% confidence level and the confidence interval (0.33… to 1.42…) are both positive, which indicates

that the relationship between distance to Metro and parking demand is significant, and not merely due to chance.

Staff found a statistically significant relationship between distance to Metrorail station (again, defined as the walking distance, not a “straight-

line” distance) and the number of vehicles per unit registered to multi-family buildings across the entire data set and for the buildings within one

mile of a Metro station.


This scatter plot describes the relationship between distance to Metro and vehicle registrations per unit of housing for entire Commissioner of

Revenue data set.

R² = 0.3129

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Vehicles Registered per Unit by Distance to Metro


This regression output describes the same data set numerically.

REGRESSION SUMMARY OUTPUT FOR ALL PROPERTIES IN THE COMMISSIONER OF REVENUE DATA SET




Regression 1 3.524805 3.524805 54.6595 2.12E-11 Residual 120 7.738391 0.064487

Total 121 11.2632


Intercept 0.751184 0.033386 22.49984 7.13E-45 0.685082 0.817287

Distance to Metro 0.216791 0.029323 7.393207 2.12E-11 0.158733 0.274848

The Significance F value is significant at the 99% confidence level and the confidence interval (0.16… to 0.27…) are both positive, which indicates

that the relationship between distance to Metro and parking demand is significant, and not merely due to chance.


The regression output for those properties only within one mile of a Metro (below) also describe a statistically significant relationship.

SUMMARY OUTPUT FOR PROPERTIES LESS THAN ONE MILE FROM A METRO STATION 1 MILE USING COMMISSIONER OF REVENUE DATA

Regression Statistics

Multiple R 0.274239

R Square 0.075207

Adjusted R Square 0.065045

Standard Error 0.248437

Observations 93

ANOVA

df SS MS F Significance F

Regression 1 0.456761 0.456761 7.400431 0.007812

Residual 91 5.616596 0.061721

Total 92 6.073357


Intercept 0.701771 0.058172 12.06379 1.41E-20 0.58622 0.817322

Distance to Metro 0.345024 0.12683 2.720373 0.007812 0.093093 0.596956

Though the R square value is low for the buildings closer than 1 mile to Metro (0.075…), the Significance F and p-value for the “Distance to

Metro” variable are significant at the 99% confidence level and the confidence interval (0.093… to 0.59…) are both positive, which indicates that

the relationship between distance to Metro and parking demand is significant, and not merely due to chance. The data set provided by Neal

Kumar did not have measurements for the distance to Metro for each property which meant that a similar analysis was not possible with that

data.

With a relationship found between parking demand and distance to a Metro station, staff then divided the available data sets into ¼-mile ranges

(0 to ¼ mile from a Metro station, ¼ mile to ½ mile from a Metro station, etc.) which are described graphically in the following chart. Staff chose

to divide the data into ¼-mile ranges because these are easily understood and commonly used thresholds in transportation planning. The

following chart describes these data sets divided into ¼-mile subsets.


Note that there were no buildings in the Site Plan Performance Monitoring data set between 0.5 and 1.0 miles from a Metro station.


The following table describes the same data numerically with data from the Resident Study also added. Note that while the entire Resident

Study data set is heavily influenced by the number of one-car households, the mean value of reported vehicles owned per household is smaller

for households closer to Metro than those far away.

Parking Demand/Vehicle Registration/Vehicle Ownership based on Distance to Transit from Various Data Sources for Multi-Family Buildings

in the Rosslyn-Ballston and Jefferson Davis Corridors

Under 0.25 Miles 0.25 to 0.5 Miles 0.5 to 0.75 Miles 0.75 to 1.0 Miles

SP COR Resident Study SP COR Resident Study SP COR Resident Study SP COR Resident Study

Highest Quartile 1.21 1.11 1.3 1.24 1.18 1.71

75 Percentile 1.06 0.89 1.04 0.93 1 1.51

Mean 0.93 0.83 0.97 0.9 0.79 1.04 0.8 1.23 1.33 1.21

Median 0.9 0.81 0.82 0.79 0.79 1.28

25 Percentile 0.81 0.74 0.78 0.65 0.58 1.05

Lowest Quartile 0.67 0.52 0.64 0.33 0.38 0.72

Number of Buildings 13 25 7 39 21 8

In order to summarize this data as a reference for the Working Group’s discussions on minimum parking requirements, staff decided to take the

average of the mean, median, 25th percentile, and lowest quartile values from the Site Plan Monitoring Study and Commissioner of Revenue

datasets. However, given that the two datasets had different numbers of buildings, staff decided to take the weighted average of these two

values based on the number of buildings in each (in other words, the Commissioner of Revenue data, as a larger data set, had more weight in

the calculations). Of course, the number of vehicles reported in the Commissioner of Revenue data is smaller than in the Site Plan studies; this is

expected in part because some households do not register their vehicles with Arlington County, and because the Site Plan studies would have

included any visitor-parking demand in the garage while the Commissioner of Revenue data would not have captured visitor-parking demand.

In order to compensate for this difference, staff compared the two data sets, and found that the difference in the median and mean values was

approximately 0.1; staff decided to adjust the Commissioner of Revenue values by 0.1 vehicles per unit in order to make the two datasets more

comparable.


The following table describes the weighted averages for the two datasets after adjusting the Commissioner of Revenue data by 0.1 vehicles per

unit.

Weighted Average Parking-Demand Values based on Distance to Metro for Multi-Family Buildings in the Rosslyn-Ballston and Jefferson Davis

Corridors

Under 0.25 Miles 0.25 to 0.5 Miles 0.5 to 0.75 Miles 0.75 Miles to 1 Mile

Highest Quartile 1.21 1.33 1.28 1.81

75 Percentile 1.01 1.03 1.10 1.61

Mean 0.93 0.89 0.90 1.43

Median 0.91 0.88 0.89 1.38

25 Percentile 0.83 0.75 0.68 1.15

Lowest Quartile 0.64 0.46 0.48 0.82

These data were the basis for the potential minimum ratios (by distance to Metro) presented at the February 6th Working Group meeting.

The Working Group was also interested in measures of transit access that would capture the value of all modes of transit, not just Metrorail.

Staff decided to refer to a data set from the University of Minnesota’s Accessibility Observatory that describes transit access to jobs for all

Census Blocks in the United States. Staff used the latest data set available, which the University released in late 2016.3 In this case, transit access

is defined as the number of jobs that can be reached by transit within 30 minutes during a weekday, morning peak period of transit service.

3 A full methodology report is available at http://ao.umn.edu/research/america/transit/2015/.

http://ao.umn.edu/research/america/transit/2015/

http://ao.umn.edu/research/america/transit/2015/


The following scatter plot describes how parking demand varies with job access at the Site Plan buildings closer than 1 mile to Metro (no

significant relationship was found when buildings more than 1 mile away from a Metro station were included).

R² = 0.2202

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Number of Jobs Accessible within 30 Minutes

Parking Demand at Arlington Site Plan Buildings by Job Accessibility on Transit, Less than 1 Mile from Metro, Bundled and Unbundled Parking Charges


This regression output describes the same data numerically.

ACCESSBIILITY BY TRANSIT; NUMBER OF JOBS REACHABLE IN UNDER 30 MIN; SUMMARY OUTPUT FOR PROPERTIES LESS THAN 1 MILE FROM METRO, BOTH BUNDLED AND UNBUNDLED PARKING



ANOVA

df SS MS F Significance

F Regression 1 0.139998 0.139998 5.365689 0.031862

Residual 19 0.495735 0.026091 Total 20 0.635733

Coefficients Standard

Error t Stat P-value Lower 95% Upper 95%

Intercept 1.222253434 0.130632 9.356478 1.52E-08 0.948838 1.495669 Jobs Accessible within 30 minutes by transit -5.46384E-07 2.36E-07 -2.3164 0.031862 -1E-06 -5.3E-08


When only properties closer than a 1-mile walk to a Metro station with unbundled parking were included, the relationship was somewhat

weaker.

R² = 0.2092

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Parking Demand at Arlington Site Plan Buildings by Job Accessibility on Transit, Unbundled Parking and Less than 1 Mile from Metro


ACCESSBIILITY BY TRANSIT; NUMBER OF JOBS REACHABLE IN UNDER 30 MIN; SUMMARY OUTPUT FOR PROPERTIES LESS THAN 1 MILE FROM

METRO WITH UNBUNDLED PARKING





Total 16 0.602018109


Intercept 1.206745376 0.147734901 8.168316162 6.65699E-07 0.891855889 1.521634862 Jobs Accessible within 30 minutes by transit -5.34745E-07 2.68465E-07

-1.991862394 0.064915254 -1.10696E-06 3.74742E-08

The R square is slightly lower and the significance F is no longer significant at the 95% confidence level (though it is still significant at the 90%

confidence level), which is why the confidence interval at 95% crosses 0.


The following scatter plot describes how vehicles registered per unit from the Commissioner of Revenue changes as job accessibility on transit

changes.

R² = 0.2684

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Number of Jobs Accessible within 30 Minutes on Transit

Vehicles Registered per Unit by Number of Jobs Accessible by Transit in 30 Minutes; All Locations


The following regression output describes the same data numerically.

VEHICLES REGISTERED PER UNIT BY NUMBER OF JOBS ACCESSIBILE BY TRANSIT IN 30 MINUTES; ALL LOCATIONS




Regression 1 3.454647 3.454647 48.78382 1.24E-10 Residual 133 9.418452 0.070815

Total 134 12.8731


Intercept 1.26418 0.051748 24.42967 5.19E-51 1.161825 1.366535

Jobs Accessible within 30 minutes by transit -8.7E-07 1.24E-07 -6.98454 1.24E-10 -1.1E-06 -6.2E-07

The Significance F value is significant at the 99% confidence level and the confidence interval are both negative, which indicates that the

relationship between distance to Metro and parking demand is significant, and not merely due to chance.


When looking at only the buildings in the Metro Corridors, vehicle registration data remains correlated to job accessibility.

R² = 0.0784

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Number of Jobs Accessible within 30 Minutes by Transit

Vehicles Registered per Unit by Number of Jobs Accessible by Transit in 30 Minutes; Within Metro Corridors Only


The correlation is weaker for the buildings within the Metro Corridors alone, but this is likely, in part, simply because there are fewer data

points. The following regression output describes the same data numerically.

VEHICLES REGISTERED PER UNIT BY NUMBER OF JOBS ACCESSIBILE BY TRANSIT IN 30 MINUTES; METRO CORRIDORS ONLY





Total 96 6.107291

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 1.045943 0.076459 13.67976 3.5E-24 0.894152787 1.19773412 Jobs Accessible within 30 minutes by transit -4.6E-07 1.62E-07 -2.84203 0.005486 -7.8029E-07 -1.38493E-07 Given the difficulty of using job accessibility by transit as a measurement for parking-minimum policy, staff decided to consider recent parking

demand data by planning area. The planning areas are commonly understood and well defined, and they also reflect differences in transit

service around the County.

The following box-and-whisker plot describes the Commissioner of Revenue data, Site Plan data, and space-rental data from Neal Kumar divided

up into station areas.


The following table describes these data numerically.

Parking Demand/Vehicle Registration/Vehicle Ownership based on Metro Corridor Planning Area

Ballston Virginia Square Clarendon Courthouse Rosslyn Pentagon City Crystal City

COR NK SP COR NK* SP COR NK SP COR NK SP COR NK SP COR NK SP COR NK SP

Highest Quartile

1.52 1.63 1.1 1.18 1.08 0.82 1.04 1.1 1.18 1.01 1.21 1.23 0.91 1.1 1.48 1.41 1.3 0.96 1.48 1

75th Percentile

1.1 1.32 1.1 1.095

1.08 0.82 1.01 1.02 0.98 0.91 1.21 0.92 0.91 1.16 1.08 1.16 0.82 1.07 1

Mean 0.89 1.19 0.98 0.95 0.83 0.71 0.9 0.84 0.89 0.79 0.97 0.79 0.78 0.85 0.93 0.97 0.77 0.95 0.89

Median 0.83 1.07 0.92 0.89 0.88 0.73 0.95 0.77 0.81 0.79 0.86 0.78 0.76 0.68 0.83 0.96 0.77 0.92 0.89

25th Percentile

0.72 0.94 0.9 0.89 0.54 0.6 0.8 0.72 0.74 0.65 0.84 0.65 0.59 0.61 0.73 0.8 0.68 0.73 0.77

Lowest Quartile

0.33 0.49 0.9 0.89 0.54 0.48 0.63 0.67 0.6 0.58 0.84 0.33 0.59 0.84 0.51 0.65 0.64 0.65 0.73 0.77

N 23 16 5 9 3 5 6 6 15 8 3 21 3 3 9 9 8 15 7 2

Highest Quartile

1.62 1.63 1.1 1.28 1.08 0.92 1.04 1.1 1.28 1.01 1.21 1.33 0.91 1.1 1.58 1.41 1.3 1.06 1.48 1

75th Percentile

1.2 1.32 1.1 1.195

1.08 0.92 1.01 1.02 1.08 0.91 1.21 1.02 0.91 0 1.26 1.08 1.16 0.92 1.07 1

Mean 0.99 1.19 0.98 1.05 0.83 0.81 0.9 0.84 0.99 0.79 0.97 0.89 0.78 0 0.95 0.93 0.97 0.87 0.95 0.89

Median 0.93 1.07 0.92 0.99 0.88 0.83 0.95 0.77 0.91 0.79 0.86 0.88 0.76 0 0.78 0.83 0.96 0.87 0.92 0.89

25th Percentile

0.82 0.94 0.9 0.99 0.54 0.7 0.8 0.72 0.84 0.65 0.84 0.75 0.59 0 0.71 0.73 0.8 0.78 0.73 0.77

Lowest Quartile

0.43 0.49 0.9 0.99 0.54 0.58 0.63 0.67 0.7 0.58 0.84 0.43 0.59 0.84 0.61 0.65 0.64 0.75 0.73 0.77

COR = Commissioner of Revenue vehicle registration data.

NK = Data provided by Neal Kumar. *Note that in this data set, Ballston and Virginia Square were grouped together.

SP = Site Plan Performance Monitoring Studies

In order to summarize this data as a reference for the Working Group’s discussions on minimum parking requirements, staff again took the

average of the mean, median, 25th percentile, and lowest quartile values from the Site Plan Monitoring Study, Commissioner of Revenue, and

space-rental datasets. Once again, staff adjusted the Commissioner of Revenue data up by 0.1 vehicles per unit.


Weighted Average Values of Parking Demand/Vehicle Registration/Vehicle Ownership based on Metro Corridor Planning Area

Ballston Virginia Square Clarendon Courthouse Rosslyn Pentagon City Crystal City

Highest Quartile 1.56 1.23 1.03 1.19 1.26 1.44 1.18

75th Percentile 1.23 1.17 0.99 1.04 0.89 1.17 0.97

Mean 1.06 1.00 0.85 0.93 0.78 0.95 0.90

Median 0.98 0.96 0.85 0.87 0.77 0.85 0.89

25th Percentile 0.87 0.88 0.74 0.78 0.65 0.74 0.75

Lowest Quartile 0.51 0.88 0.63 0.68 0.49 0.63 0.75

The values between the mean and lowest quartile served as the basis for the proposed range of new parking minimums shown at the February

6th Working Group meeting.

Residential Parking Working Group Meeting Ten Read-Ahead … · Capital Bikeshare station in lieu...

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