Transit Fare Elasticity A WMATA Experience Shi Shelley Xie WMATA

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Transit Fare Elasticity A WMATA ExperienceShi
(Shelley) XieWMATA
11th TRB National Transportation Planning
Application Conference Daytona Beach, Florida May
6-10, 2007
The views expressed herein are solely of the
presenter and do not necessarily reflect the
policies or positions of WMATA Board or its
management team
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Outline

• Background
• Fare Structure
• Metrorail Ridership
• Methodology
• Forecast vs. Actual
• Conclusions

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Background

• September 11, 2001 tragedy impact
• Slowdown in regional economy
• slowing growth in weekday commuter trips
• flattening growth in weekday off-peak and weekend
  trips
• stalling growth in non-passenger revenues
• Slowdown in national economy
• reducing growth in tourism related trips
• lacking major events and related trips
• Sharply raised operating expenses for Fiscal Year
  2004 and 2005
• First fare change in 7 consecutive years

Fare Increase For FY04/FY05 Was Inevitable
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Background (cont)

• Metrorail passenger revenue is about 70 percent
  of all passenger revenues
• Metrorail ridership data-warehouse became
  available and reliable
• Time-of-day data
• Weekday peak (AM and PM)
• Weekday off-peak (Mid-day and Evening)
• Distance-based O/D data
• Ridership by fare media data
• However, Metrobus data lagged
• During the transition of changing fare collection
  technology

Metrorail Only Discussion
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Washington Metropolitan Area Transit Authority
Metrorail System
Metrorail System 106.3 Miles 86
Stations FY2006 Ridership 205
million Average Weekday Ridership (FY06)
750,000 2/3 peak trips FY2007 Operating
Budget 620.8 million
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Fare Structure

• Metrorail fare structure is a distance and time
  based fare system
• Regular fare (or Peak Fare) includes a base
  boarding charge, tier mileage charge and maximum
  fare elements
• Discounted fare (or Off-Peak Fare) includes three
  flat fares for each fare segment

Fare Elasticity Should Reflect Distance-based
Structure
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Fare Structure (cont)

• Limited discount products
• Passes
• Day Pass
• Weekly Short-Trip Pass
• Weekly Pass
• Convention pass
• Other discount fare products
• Elderly and Disabled
• DC Student fare and Student SmartPass

Discount Products Accounts For Only A Small of
Used Fare Media
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Understanding Metrorail Ridership

• Metrorail ridership growth rate is fairly stable
• Steady federal or federal government related
  employment
• Very stabled commuter market about 40 federal
  workers
• Constant tourist stream
• Smithsonian museums and national park service
  attractions
• Metrorail discretionary ridership follows a
  seasonal pattern in a 12 months circle
• Summer vacation-local vacation-Spring
  breaks-summer vacation
• Metrorail plays important roll in all special
  events
• National events
• Million Man March
• State Funerals
• Local events
• Sports games, concerts, etc.

Fare Elasticity Should Be Low Compared to Other
Markets
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Understanding Metrorail Ridership

• Distance-based trip distributions were fairly
  consistent
• Roughly 50 of weekday peak trips were in base
  fare and 1st tier fare segment (zero 6 miles)
  the rest of weekday peak trips were split almost
  evenly between 6-10 mile segment and beyond 10
  mile segment
• Weekday off-peak periods and weekend trips had
  over 60 in 1st fare segment, about 16 in 2nd
  fare segment and about 20 in 3rd fare segment

Fare Elasticity Matrix Would Break Upon Those
Trip Lengths
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Methodology - Things to consider

• Lack of research or study on transit fare
  elasticity, especially on distance-based fare
  elasticity
• WMATAs last fare elasticity study was done in
  early 1990s
• The study was intended to do distance-based fare
  elasticity
• Lack of data was the main hurdle in accomplishing
  the task
• WMATAs unique market
• Large percentage of federal government commuters
• Very seasonal ridership pattern, influenced by
  tourism
• Unique transit incentives
• MetroChek / SmartBenefit
• Most federal and some private employees receive
  110 per month
• Budgetary request
• Measure revenue impact for every fare element

Create Customized Fare Elasticity Matrix
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Methodology - Assumptions

• Assumptions
• Fare elasticity for Metrorail would be lower than
  transit industry standards
• Strong and stable commuter market
• High transit incentive
• Weekday trips would be less elastic than that for
  weekend trips
• Small portion of discretionary trips on weekdays
• High percentage of discretionary trips on
  weekends
• Suburban long-distance commuter trips would be
  less elastic than short-distance trips
• Less alternatives for long-distance trips long
  and unpredictable commuting time as the results
  of traffic congestion, lack of downtown parking,
  etc.
• More alternatives for short-distance trips taxi,
  bike, walk, etc.

Bases for Elasticity Matrix
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Methodology - Elasticity Matrix

• Elasticity (k-factor) matrix includes the
  following variables time-of-day pricing, trip
  length and percent fare change
• For weekday regular (peak) fare

• For weekday off-peak discount fare

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Methodology - Elasticity Matrix (cont)

• Elasticity (k) matrix includes the following
  variables time-of-day pricing, trips length and
  percent fare change
• For weekend discount fare

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Methodology - Fare Model

• Fare model to reflect WMATAs fare structure
• Can measure revenue impact for each fare segment

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Methodology - Fare Model (cont)

• First, validating fare model with no-fare impact
  projection
• Matching total targeted ridership and revenue
  figures

• Then, inputting fare change proposals into model
• One or multiple fare elements

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Methodology - Fare Model (cont)

• Sample of the application

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Fare Package for FY04 and FY05

• Regular (peak) fare

• Discount (off-peak) fare

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Fare Package for FY04 and FY05 (Cont)

• FY2004 fare increase penalized long distance
  trips
• Less than 10 increase on base fare and discount
  fare, but
• More than 10 increase on 1st and 2nd tier
  mileage charge and max-fare
• FY2005 fare increase had a reverse impact
• More than 10 base fare increase
• Less than 10 increase on 1st and 2nd tier
  mileage charge and max-fare
• For both years, the fare matrix was essential to
  dealing with variety of fare changes, and helped
  to create accurate projections

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Forecast vs. Actual

• Actual ridership for the two fiscal years that
  had fare increases were very close to projections
  

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Ridership - After Fare Changes

• Distance-based trip distributions were mostly
  consistent with the pattern before fare change
  with expected fare elasticity impact
• Share of trips with the highest elasticity (0-6
  mile) dropped most
• Share of trips between 6 and 10 miles changed
  slightly
• Share of trips traveled beyond 10 miles increased
• Partly due to new Largo extension opening
• Weekday off-peak and Saturday ridership were
  mostly consistent with the pattern before fare
  change
• Sunday ridership distribution changed the most,
  reflecting the discretionary and unpredictable
  trip nature of these types of trips

Elasticity Matrix Seems Valid
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Ridership The Differences

• Post fare change distribution

• The difference (compared to the table on Slide 9)
  

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Conclusions and Suggestions

• It is an empirical study.
• Further improvement of the fare model is needed
• Maxfare how to deal with maxfare which is
  discounted fare
• Cross-elasticity how to combine parking fee
  increase with fare increase
• Further econometric analysis is needed
• Especially for distance-based fare structure

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Acknowledgement

• Thanks to my colleagues in Office of Management
  and Budget Services for their suggestions and
  support
• Thanks to my current director for giving me the
  green light to finish my old job.