Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 1 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).
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 2 of 29
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
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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
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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)
Station with bikes and 15 docks $50,000 $1,600 ($19,200)
Station with bikes and 19 docks $60,000 $2,100 ($25,200)
*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
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 5 of 29
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
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 6 of 29
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):
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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
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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.
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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.”
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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
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 0.5 1 1.5 2 2.5
Veh
icle
s/U
nit
Distance from Metro (miles)
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)
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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.
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 13 of 29
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
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 0.5 1 1.5 2 2.5 3
Veh
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s R
egis
tere
d p
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nit
Distance to Metro (miles)
Vehicles Registered per Unit by Distance to Metro
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This regression output describes the same data set numerically.
REGRESSION SUMMARY OUTPUT FOR ALL PROPERTIES IN THE COMMISSIONER OF REVENUE DATA SET
Regression Statistics Multiple R 0.559418
R Square 0.312949 Adjusted R Square 0.307223 Standard Error 0.253942 Observations 122
ANOVA df SS MS F Significance F
Regression 1 3.524805 3.524805 54.6595 2.12E-11 Residual 120 7.738391 0.064487
Total 121 11.2632
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
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.
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 15 of 29
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
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
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.
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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.
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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.
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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/.
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 19 of 29
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
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
0 100000 200000 300000 400000 500000 600000 700000 800000
Max
imu
m N
um
ber
of
Veh
icle
s P
arke
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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
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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
Regression Statistics Multiple R 0.469270652
R Square 0.220214945 Adjusted R Square 0.179173627 Standard Error 0.16152811 Observations 21
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
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 21 of 29
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
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
0 100000 200000 300000 400000 500000 600000 700000 800000
Max
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m N
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Number of Jobs Accessible within 30 Minutes
Parking Demand at Arlington Site Plan Buildings by Job Accessibility on Transit, Unbundled Parking and Less than 1 Mile from Metro
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ACCESSBIILITY BY TRANSIT; NUMBER OF JOBS REACHABLE IN UNDER 30 MIN; SUMMARY OUTPUT FOR PROPERTIES LESS THAN 1 MILE FROM
METRO WITH UNBUNDLED PARKING
Regression Statistics Multiple R 0.45735571
R Square 0.209174245 Adjusted R Square 0.156452528 Standard Error 0.17815563 Observations 17
ANOVA df SS MS F Significance F
Regression 1 0.125926683 0.125926683 3.967515799 0.064915254 Residual 15 0.476091426 0.031739428
Total 16 0.602018109
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
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.
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 23 of 29
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
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
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0 100000 200000 300000 400000 500000 600000 700000 800000
Veh
icle
s R
egis
tere
d P
er U
nit
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
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 24 of 29
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 Statistics Multiple R 0.518036
R Square 0.268362 Adjusted R Square 0.262861 Standard Error 0.266112 Observations 135
ANOVA df SS MS F Significance F
Regression 1 3.454647 3.454647 48.78382 1.24E-10 Residual 133 9.418452 0.070815
Total 134 12.8731
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
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.
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 25 of 29
When looking at only the buildings in the Metro Corridors, vehicle registration data remains correlated to job accessibility.
R² = 0.0784
0
0.2
0.4
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0.8
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0 100000 200000 300000 400000 500000 600000 700000 800000
Veh
icle
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d p
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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
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 26 of 29
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
Regression Statistics Multiple R 0.279929
R Square 0.07836 Adjusted R Square 0.068659 Standard Error 0.243413 Observations 97
ANOVA df SS MS F Significance F
Regression 1 0.478569 0.478569 8.077158 0.00548649 Residual 95 5.628722 0.05925
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.
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 27 of 29
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 28 of 29
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.
Residential Parking Working Group Meeting Ten Read-Ahead Materials Page 29 of 29
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.