Social Equity in Distance Based Fares

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Social Equity in Distance Based Fares
Steven Farber, University of Utah
GIS in Transit
October 16-17, 2013
Background
• UTA transitioning from a flat fare to a distance
based fare
• Title VI and EJ requirements (differential impacts)
• Different populations will be impacted differently
since trip gen’s and distances travelled vary
systematically with demographics
• Understanding of travel behavior required to assess
differential impacts
Fares and Sustainability
• Competing goals of transit agency
o Economic – Increase Revenue
o Environmental – Decrease automobile use
o Social – Provide transit service to those in need or equally
to all
• Distance based fares
o Economically efficient (capturing external costs)
o Socially beneficial (shorter but more frequent trips)
o Environmentally detrimental (increased costs for long
distance discretionary riders)
Fares and Equity
Social equity in transportation is summarized by Sanchez
as the distribution of “benefits and burdens from transportation projects
equally across all income levels and communities”
Fairness: Whether the costs and benefits are equal after taking needs,
means, and abilities into consideration.
Are we interested in equality or fairness?
Equity

Fairness


/


Transit Fare
Taxation
Flat fare per trip
Fixed tax dollar amount
regardless of income
Distance based fare
(DBF)
Single tax rate for all
DBF with reduced cost
of successive starts
Progressive tax rates (sliding
scale)
Research Questions
• What are the social equity and fairness impacts of a
transition to DBF?
• How can travel behavior be used to assess social
equity in this case?
• If DBFs are generally desirable, how can we find
and address exceptions to this rule?
Data
• Utah Household Travel Survey
o
o
o
o
o
Spring 2012
1 day travel diary
9,155 Households
27,046 People
101,404 Trips
• Filtered to only those residing in UTA’s core service
area - 68% of respondents
• # daily of transit trips
• Daily distance travelled by transit
Observed Travel Behavior
Ridership
Percentage
Household Income
No Answer
Under $35,000
$35,000 - $49,999
$50,000 - $99,999
$100,000 or more
Hispanic
Yes
No
Prefer not to answer
Race
White or Caucasian
All other
Age
18-24 years old
25-34 years old
35-44 years old
45-54 years old
55-64 years old
>65 years old
Distance
Trips Travelled
(miles)
1.91
5.17
2.94
2.24
2.17
1.73
1.94
1.88
1.85
1.86
20.09
13.25
19.14
20.56
24.51
4.38
2.60
3.02
1.89
1.88
1.64
17.39
19.60
5.34
2.50
4.61
1.89
1.77
19.93
14.25
6.87
4.07
3.37
3.49
3.13
1.59
1.82
1.88
1.84
1.87
1.88
2.08
17.47
17.51
24.19
19.73
17.82
13.91
Ridership
Percentage
Education
High school or less
Some College/Vocational/Associates
Bachelors
Grad/Post Grad
Licensed
Yes
No
Number of vehicles
Zero vehicle household
1 vehicle household
2 vehicle household
3+ vehicle household
Home Ownership
Rent
Own
Other
Residence Type
Single-family house (detached house)
Apartments
Other
Distance
Trips Travelled
(miles)
3.73
3.40
2.90
5.26
1.85
1.87
1.80
1.95
13.08
18.56
19.34
21.88
3.14
13.67
1.86
1.96
20.39
11.34
27.34
5.40
1.87
1.99
2.26
1.78
1.89
1.81
7.42
14.04
23.70
23.21
5.56
2.17
0.77
1.86
1.87
2.00
11.66
22.77
17.61
2.17
6.17
3.34
1.88
1.92
1.74
22.82
11.38
13.89
Selection of Fares
• UTA is considering fares that consist of a flat
component and a distance-based component
• For this study, we selected a revenue-neutral fare
o $0.50 + $0.19 per mile
• Distance is measured as Euclidean distance so that
users are not penalized by indirect network design
Transit
Trips
Household Income
Under $35,000
$35,000 - $49,999
$50,000 - $99,999
$100,000 or more
Hispanic
Yes
No
Race
White or Caucasian
All other
Age
18-24 years old
25-34 years old
35-44 years old
45-54 years old
55-64 years old
>65 years old
Distance
Travelled
Flat
Fare
Distance Percentage
Based Fare
Change
1.94
1.88
1.85
1.86
13.25
19.14
20.56
24.51
4.85
4.70
4.63
4.65
3.49
4.58
4.83
5.59
-28.1%
-2.6%
4.5%
20.1%
1.89
1.88
17.39
19.6
4.73
4.70
4.25
4.66
-10.1%
-0.8%
1.89
1.77
19.93
14.25
4.73
4.43
4.73
3.59
0.1%
-18.8%
1.82
1.88
1.84
1.87
1.88
2.08
17.47
17.51
24.19
19.73
17.82
13.91
4.55
4.70
4.60
4.68
4.70
5.20
4.23
4.27
5.52
4.68
4.33
3.68
-7.0%
-9.2%
19.9%
0.2%
-8.0%
-29.2%
Method
• Estimate a joint model of transit trip generations and
distance travelled
• Spatially expanded coefficients – controls for
contextual information not captured in the dataset
• Convert travel behavior to fares and compare
results
Ordered Model Estimates
Continuous Model Estimates
B
0.9052
p-value
0.0000
-0.1468
0.0607
2
1.4356
2
Age less than 17 years
Age 18-24 years
Age over 65 * 
B
-3.9564
p-value
0.0000
Two transit trips
1.1045
0.0000
0.0193
Three transit trips
1.5018
0.0000
Constant
Age over 65 * 
Mobility Limitation * 
0.8022
0.0167
Age over 65
-0.2700
0.1709
-0.3858
0.0956
No children or retirees
-0.1596
0.0821
Household w/ retirees
-0.3449
0.0004
Student, em. < 25 hrs/week
-0.2771
0.1868
0.9011
0.0000
Unemployed/retired
-0.2670
0.0483
-0.4158
0.0000
High school or less * D_CBD
-0.0072
0.0619
-1.1084
0.2099
Hispanic - Refusal
-0.5133
0.0464
1.8918
0.0003
Race: non-white * D_CBD
0.0199
0.0052
-0.3251
0.0000
Race: non-white * 
-0.4355
0.1926
0.1842
0.0001
Zero vehicles * D_CBD
-0.0132
0.0275
Self employed
Student, emp. 25+ hrs/week
Unemployed/retired * 
Unemployed/retired * 
2
Grad. or post-grad. degree
Female
1.1440
0.0446
Income < $25K * 
-0.8834
0.0818
-1.2459
0.0902
Income $75-$100K
-0.4831
0.0001
-2.4449
0.0011
4 people
0.1759
0.1779
-1.3931
0.0000
3+ workers
0.2371
0.0917
No driver’s license * 
1.4272
0.0016
3+ children’s bikes
0.2400
0.1706
Zero vehicle household
-0.7612
0.0000
5+ years in current res.
0.1163
0.1723
2 vehicle household
0.3455
0.0000
City, residential neigh.
-0.1825
0.0500
3+ vehicle household
0.5623
0.0000
Distance to CBD
0.0522
0.0000
Income < $25K * 
-0.5827
0.1106
Distance to bus stop
0.1421
0.1002

Hispanic
Hispanic * 
2
Hispanic * 
No driver’s license
Income – Refusal
Household rents * D_CBD
Household rents * 
Household Tenure - Other
2
0.0087
0.0001

-1.1271
0.0004
Joint Model Parameter Estimates
0.6485
0.1282
0.1788
0.0003
17.4178
0.0000
-15.0180
0.0000
p-value
0.0000
0.8261
0.0700
1
B
-1.3672
-0.1580
0.0740
2
-1.5167
0.0000
0.2259
0.0186
3
-2.4753
0.0000
6+ people
-0.2206
0.0076

-0.1883
0.0625
Suburban mixed neighborhood
-0.1226
0.0211

0.8322
0.0000
0.0024
0.2466
0.4476
0.0410
Household Tenure - Refusal
3+ workers
3+ children’s bikes
Distance to Commuter Rail
2
2
Log-Likelihood: -3701.04; p( )<0.0000; McFadden’s Adj. 2 : 0.1144; pseudo- 2 =0.5782; =16071
A
B
Low Income
& Education
Ref.
0
10 Miles
C
0
10 Miles
Low Income
& Elderly
0
Distance Travelled (Miles)
<2
5-6
9 - 10
22 - 26
40 - 45
2-3
6-7
10 - 14
26 - 30
45 - 55
3-4
7-8
14 - 18
30 - 35
> 55
4-5
8-9
18 - 22
35 - 40
Bus Routes
D
10 Miles
Non-White
0
10 Miles
A
B
Low Income
& Education
Ref.
0
10 Miles
C
0
10 Miles
Low Income
& Elderly
0
Ratio of Expected Distance Based Fare to Flat Fare
< 0.35
0.65 - 0.75
1.05 - 1.25
2.50 - 3.00
0.35 - 0.45
0.75 - 0.85
1.25 - 1.50
3.00 - 4.00
0.45 - 0.55
0.85 - 0.95
1.50 - 2.00
> 4.00
0.55 - 0.65
0.95 - 1.05
2.00 - 2.50
Bus Routes
D
10 Miles
Non-White
0
10 Miles
Census Tracts
Most Non-White
Most Hispanic
Cost Ratio
< 0.95
0.95 - 1.05
1.05 - 1.25
1.25 - 1.50
1.50 - 2.00
2.00 - 2.50
2.50 - 3.00
3.00 - 4.00
> 4.00
Conclusions
• Distance based fares generally result in cheaper fares for
those who need it most
• Pockets of mismatch exist – suburbanization of the poor
poses a problem
• Burden on long-distance low-income travellers can be
mitigated through reduced flat-fare components
• Changes in price are likely to shift wealthy discretionary
riders back to their cars, but it may attract a plethora of
new low-income riders
Next Steps
• Developing a GIS Decision Support System
• Analysts at UTA can compute fare surfaces for
different demographic profiles and different fare
structures
• Targeted studies of riders in particular at-risk
neighborhoods identified by the DSS
Acknowledgements
•
•
•
•
Xiao Li, Graduate RA
Keith Bartholomew, UofU
Antonio Páez, McMaster University
Khandker M. Nurul Habib, University of Toronto
• Utah Transit Authority
• Partial support from:
o National Institute for Transportation and Communities (DTRT12-G-UTC15540)
o National Science Foundation (BCS-1339462)
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