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Defining and measuring
public transit service equity
Alex Karner, PhD
Postdoctoral researcher ● CEDEUS
Defining and measuring
public transit service equity
Alex Karner, PhD
Postdoctoral researcher ● CEDEUS
@AlexKarner
Transportation equity What is it?
3
• Freeway revolts, urban
unrest (1960s)
– McCone commission
report
• ISTEA (1991)
– “Planning factors”
• Subsequent guidance,
legislation, etc. Watts, Los Angeles, 1965
Transportation equity What is it? • Transportation equity is a civil and human rights
priority. Access to affordable and reliable transportation widens opportunity and is essential to addressing poverty, unemployment, and other equal opportunity goals such as access to good schools and health care services. However, current transportation spending programs do not equally benefit all communities and populations. And the negative effects of some transportation decisions— such as the disruption of low-income neighborhoods — are broadly felt and have long-lasting effects. Providing equal access to transportation means providing all individuals living in the United States with an equal opportunity to succeed.
4
http://equitycaucus.org/sites/default/files/Testim
ony%20for%20the%20Record%20from%20Equ
ity%20Caucus%20for%20Jan142014House%2
0TandIHear....pdf
…a civil and human rights priority. Access to
affordable and reliable transportation
widens opportunity. Current transportation
spending programs do not equally benefit
all communities and populations. Negative
effects of some transportation decisions are
broadly felt and long-lasting.
The Leadership Council on Civil Rights
http://www.civilrights.org/transportation/
Transportation equity What is it? An illustrative example
• 2004: Minority Citizens Advisory Committee proposes adoption of four EJ principles:
1. Creation of an empowering public process
2. Collection of data to analyze inequities in transportation funding
3. Changing discretionary investments to mitigate such inequities as are found
4. Mitigation of disproportionate project effects prior to being approved for funding
5 Transportation 2035: MTC’s 2009 RTP
Regional equity analysis
• Critical review
– Geographic aggregation
– Future vs. existing equity
– Treatment of race
6
Karner, A. and D. Niemeier (2013). “Civil rights guidance and equity analysis methods for regional
transportation plans: a critical review of literature and practice.” Journal of Transport Geography 33: 126-134.
Rowangould, D., A. Karner and J. London. “Identifying environmental justice communities for transportation
analysis.” Under review at Transportation Research: Part A.
San Francisco County, California
9
Why do we have transit?
• Image and aesthetics
• To use federal funds (FTA’s New Starts)
• Economic development (Chatman and Noland, 2013)
• Congestion and air quality mitigation (Anderson, 2014)
• To provide basic mobility for transit dependent populations (Grengs, 2005; Garrett and Taylor, 1999)
Who uses transit?
10
Transit dependents
(bus users)
Choice riders
(commuter rail users)
“Simply put, the bus is the mode of the poor.”
Median income $22,500 $62,500
source: Taylor and Morris, 2015 using 2009 NHTS data
Transit goals in tension
• Rail transit service has expanded faster than bus service over the past 25 years
• Bus patronage declined from 2001-2009 as rail ridership grew
• Bus and rail service and patronage converging over time
• Shift to serving choice riders with premium service
11 sources: Taylor and Morris, 2015; Wells and Thill, 2012; Grengs, 2005
• IT ISN’T JUST ABOUT BUS V. RAIL –
CONSIDER ADDING A SLIDE WITH LIT
REVIEW ABOUT OVERALL DISPARITIES
BY NEIGHBORHOOD, EVEN FORLOCAL
BUS (WELLS AND THILL).
“No person in the United States shall, on the
ground of race, color, or national origin, be
excluded from participation in, be denied the
benefits of, or be subjected to discrimination
under any program or activity receiving
Federal financial assistance.”
Title VI of the 1964 Civil Rights Act
13
Fund recipients may not discriminate “with
regard to the routing, scheduling, or quality of
service … furnished” to patrons.
49 CFR §21.5 Appendix C(a)(3)(iii)
14
Equity analysis practice
• Transit agencies evaluate the equity of “major” service changes according to FTA guidance FTA Circular 4702.1B
• Process has been contentious in Los Angeles and the Bay Area
• FTA requires specific data and methods but these may not reflect actual ridership and afford wide agency discretion
15
image source: Metropolitan
Transportation Commission
Oakland Airport Connector
• $500 million project
• Doubles fare, no intermediate stops
• $70 million withdrawn by Federal Transit
Administration for civil rights violations
Local bus service cuts
• AC Transit disproportionately serves riders of color
• 2008-11: 8% fewer service miles, 12% fewer trips, fares
increased 11%
17
April 24, 2012: Metro responds: http://goo.gl/XNMcVx
July, 2010 Metro budget cuts 387,500 bus service hours
Nov., 2010 Bus Riders Union files administrative complaint
with Federal Transit Administration
April 23, 2012 Metro found in violation
April 5, 2013 New Metro service equity analysis
June 27, 2013 Metro found in compliance
Los Angeles
Study area: Phoenix, Arizona
• 6th largest city in US (1.4 million people)
• 12th largest metropolitan area (4.2 million people)
• Urbanized area increased sevenfold from 1950 – 2000
• 2.4% of workers commute using transit (half the US average rate)
19
Congestion on Interstate 10 in Phoenix
image source: ADOT
Typical analysis: 1. Establish service area demographics
• Based on proximity to stops
• Results may not reflect ridership and do not reflect the importance of each line to protected populations
• Academic literature takes a similar approach (REFS) – no systematic analysis of differences
21
total population people of color household
income < $25K
Valley Metro system
demographics (buffers) 1,710,309
891,990 177,640
52% 28%
Valley Metro system ridership 242,687 136,729 122,532
56% 50%
Typical analysis: 2. Establish affected population demographics
22
Route 39 – 40th St. white people of color household income < $25K
77% 23% 17%
3. Compare affected and service area populations white people of color
household income <
$25K
Valley Metro system
demographics (buffers) 48% 52% 28%
Route 39 – 40th St. 77% 23% 17%
77% > 48% and 17% < 28% Potential impact
Typical analysis: limitations
23
• Either census data or ridership can be used
• Most analyses based on demographics proximate to stops
• Academic literature has taken a similar approach (Wu et al., 2003; Minocha et al., 2008; Mavoa et al., 2012;
Al Mamun and Lownes, 2011)
Accessibility-based analysis?
• Accessibility measures the potential to
meet desired needs (Wachs and Kumagai, 1973; Handy and Niemeier, 1997)
• Essential for understanding transportation
system benefits (Martens, 2012; Martens et al. 2012)
• Use to supplement demographic analyses
24
Research questions
1. How consistently do existing methods
characterize the equity of transit-related
decisions?
2. How can new data sources aid with
equity determinations?
– Incorporate accessibility
25
Data and methods
• Map of buffers, with an inset of the census
blocks overlaid
27
• Census demographics
– 2010 SF1 (race)
– 2008-2012 ACS (income)
• Ridership
– 2010-2011 Valley Metro
On-board survey
Karner, A. and A. Golub (In press). “Comparing two common
approaches to public transit service equity evaluation.”
Transportation Research Record.
29
BRT
Rapid
Neighborhood
circulator
Rural
• White ridership higher than census on premium modes
• Black ridership higher than census on local modes
• Latino ridership lower than census in all cases
Modeling results
dependent variable (ridership proportion)
White Black Latino Asian < $25K > $50K
census 2.1 2.1 24.8
N 92 92 92 92 92 92
R2 0.26 0.024 0.33 0.20 0.003 0.005
32
• Census demographics have some
relationship with ridership for some groups
Modeling results
dependent variable (ridership proportion)
White Black Latino Asian < $25K > $50K
census 2.1 2.1 24.8
N 92 92 92 92 92 92
R2 0.26 0.024 0.33 0.20 0.003 0.005
33
dependent variable (ridership proportion)
White Black Latino Asian < $25K > $50K
census 1.89 12.43 1.76 27.6 2.61
total ridership -0.0419 0.0343 0.0303
mean walk score along
route 0.00829 -0.0184 0.0252
premium mode dummy 0.739 -1.479 -0.546 -1.77 1.58
light rail dummy 1.516 -1.418 -1.258 -1.94
N 92 92 92 92 92 92
R2 0.59 0.54 0.47 0.26 0.52 0.68
• Adding quality-of-service variables improves fit
• Relationships differ by racial category
Implications for FTA equity analysis Rapid routes example
Comparison populations
white people of
color
household
income < $25K
6,524 7,158 1,396
48% 52% 48%
Reference population (buffers)
48% 52% 28%
35
Comparison populations
white people of
color
household
income < $25K
1,337 434 205
76% 24% 11.5%
Reference population (ridership)
44% 56% 50%
Census demographics Ridership
48% = 48% and 48% > 28%
No impact under service
improvement
76% > 44% and 11.5% < 50%
Potential impact under service
improvement
Conclusions
• The demographic data used (census or
ridership) can affect the conclusions drawn
regarding equity
• FTA considers both sources valid
• Future work to understand when model
results can be more widely applied
36
Challenges
• Data on transit accessibility difficult to
acquire
• Based on regional travel demand model
outputs
• Coarse and aggregate, zone-based
• For FTA, need route- and stop-level
information
38
Maricopa Pinal
Maricopa Association of Governments TAZ system
~1 sq. mile
~1/4 sq. mile
~1/10 sq. mile
Data
39
Pedestrian network
OpenStreetMap
Block-level demographics
Longitudinal Employer Household Dynamics
Transit stops
Transit routes
GTFS
Transit schedule
Methods
1. Calculate pedestrian service areas around stops (1/4 mi. bus, 1/2 mi. rail)
2. Develop service area demographics
3. Calculate travel time between all stop pairs (64 minute cutoff, ~95% of observed trips)
1. 2 hour morning peak, 24 random departures (22 GB, ~7 hours on consumer hardware)
2. ESRI network analyst with “Add GTFS to a Network Dataset”
4. Calculate stop- and route-level accessibility
40
• In the prior slide we need a piece about EQUITY.
• Disproportionate burden is typically based on comparing sub-system ridership to systemwide numbers (%s and absolute)
• It might involve looking at the same numbers for middle and higher income groups…yeah, that makes sense, to see how those things change, but we know that lower income people are more transit dependent.
41
Origin
Stopi
Stop1 Stop2 Stopj
Workersw Jobsw
Travel time and geography
General Transit Feed Specification
Accessibility analysis would better
match proposed changes to actual
effects on transit-dependent
populations.
…
Independent from the concentration of minorities / low-income people
Develop an indicator for each stop – showing those that provide high levels
of access. Could be used as a decision support tool, instead of an after-the-
fact analysis
Demographics
Longitudinal Employer-Household Dynamics
Unemployment effects
Block group economic data
Differential transit use rates
Cost per unit of accessibility – would be cheaper for bus.
Jobsw Jobsw
How much more likely are transit
users to be low-income people?
Transit route k
𝐴𝑖𝑤 = 𝐸𝑗
𝑤𝑒−𝛽 𝑡𝑖𝑗
𝑗
Territorial accessibility
𝐴𝑖𝑤 = 𝑊𝑖
𝑤 𝐸𝑗𝑤𝑒−𝛽 𝑡𝑖𝑗
𝑗
Worker-weighted accessibility
Sum over all workers
for overall accessibility
Take mean over stops
on a route for route-
level accessibility
ti1 t12 t2j
44
Compare ACS and
LEHD data on light
rail buffers – are
there more high
income people
there?
Is it the effect of
unemployed inflating
the low-income
numbers?
Route-level worker-weighted accessibility
Scottsdale
Neighborhood
Circulators
Glendale Urban
Shuttles
Scottsdale
Neighborhood
Circulator
Glendale Urban
Shuttles
Rapid routes
45
Implications for FTA analysis
> average low-income riders
worker-weighted route-level accessibility to low-wage jobs
Limitations
• Coarse (and
unchanging) LEHD
thresholds
– Low-wage jobs
definitely low
– Mid-wage jobs less
clear
• No consideration of
unemployed
46
• LEHD also contains race, occupational category
• Updated annually
• Possible to open source the methods to some degree
Opportunities
Conclusions
• Valley Metro routes appear mostly equitable
• New data allow for the development of
refined indicators of public transit accessibility
• Their application in concert with traditional
demographic measures is likely to improve
public transit decision making
47
Contact Alex Karner
http://www.alexkarner.com
@AlexKarner
Acknowledgements Funders
Walton Sustainability Solutions Initiatives
Centro de Desarrollo Sustentable Urbano
California Endowment
Resources Legacy Fund
Sustainable Transportation Center, UC Davis
Colleagues
Deb Niemeier Parisa Fatehi-Weeks
Aaron Golub Lindsay Imai
Jonathan London Richard Marcantonio
Sam Tepperman-Gelfant