Webinar Session: “Public Transit Service Equity: Definition and Measurement Considerations” (July, 3rd). Presented by Dr. Alex Karner.

1. Defining and measuring
public transit service equity
Alex Karner, PhD
Postdoctoral researcher ● CEDEUS

2. Defining and measuring
public transit service equity
Alex Karner, PhD
Postdoctoral researcher ● CEDEUS
@AlexKarner

3. 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

4. Transportation equity
What is it?
4
…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/

5. 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

6. 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

7. Photo credit:
Paloma Pavel
Regional equity organizing:
Equity, environment and jobs Scenario

8. Transit service equity
8

9. 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)

10. 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

11. 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
11sources: Taylor and Morris, 2015; Wells and Thill, 2012; Grengs, 2005

12. Title VI of the 1964 Civil Rights Act
Legal basis for transit equity
12

13. “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

14. 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

15. 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

16. 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. 17
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

18. How well do existing methods
work?
18

19. 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

20. 20
Rapid
Express Local
Light rail

21. Typical analysis:
1. Establish service area demographics
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%

22. 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

23. 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)

24. 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

25. 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

26. Observed ridership and census
demographics
26

27. Data and methods
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.

28. 28
Rapid
Express
Local

29. 29
• White ridership
higher than
census on
premium modes
• Black ridership
higher than
census on local
modes
• Latino ridership
lower than
census in all
cases

30. 30

31. 31

32. 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

33. 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

34. Modeling results
34

35. 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

36. 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

37. Equity in accessibility
37

38. 38
Maricopa Pinal
Maricopa Association of Governments TAZ system
~1 sq. mile
~1/4 sq. mile
~1/10 sq. mile

39. Data
39
Pedestrian network
OpenStreetMap
Block-level demographics
Longitudinal Employer Household Dynamics
Transit stops
Transit routes
GTFS
Transit schedule

40. 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

41. 41
Origin
Stopi
Stop1 Stop2 Stopj
Workersw Jobsw
Travel time and geography
General Transit Feed Specification
…
Demographics
Longitudinal Employer-Household Dynamics
Jobsw
Jobsw
Transit route k
𝐴𝑖
𝑤
= 𝐸𝑗
𝑤
𝑒−𝛽 𝑡 𝑖𝑗
𝑗
Territorial accessibility
𝐴𝑖
𝑤
= 𝑊𝑖
𝑤
𝐸𝑗
𝑤
𝑒−𝛽 𝑡 𝑖𝑗
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

42. Results: total accessibility
42
Territorial accessibility Worker-weighted accessibility

43. 43
Route-level territorial accessibility

44. 44
Route-level worker-weighted accessibility
Scottsdale
Neighborhood
Circulators
Glendale Urban
Shuttles
Scottsdale
Neighborhood
Circulator
Glendale Urban
Shuttles

45. 45
Implications for FTA analysis
> average low-income riders
worker-weighted route-level accessibility to low-wage jobs

46. 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

47. 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

48. Contact
Alex Karner
alex.karner@gmail.com
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