Case study: Improving public transport supply

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Case study Improving public transport supply

• Odd I Larsen, HiMolde
• CBA-course, Molde, Dec, 2006

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A digression on interdependent projects or
measures

• In transport as well as other fields we may have
  several investments projects or other measures
  under consideration.
• Interdependence we might in a formal way state
  as NPV(A) NPV(B) ? NPV(A U B)
• i.e. the net present value of project A added to
  the present situation (without B) the net
  present value of project B (without A) is
  different from the net present value of both
  projects added to the present situation.
• Interdependence usually stems from the benefit
  side, but there may also be cases where
  construction or operating costs are involved.

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How do we handle this situation?

• We must investigate all permutations of projects
  to arrive at the combination with the highest
  NPV.
• This investigation will also tell us what are the
  best way of phasing the projects in time
  (provided that they can not be completed at the
  same time.)

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A real example 4 road projects in Oslo
Annual benefits of four projects when they are
added as the first and the last to the present
network. (Mill NOK )1)
1) Value of time savings and savings in vehicle
operating cost.
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Example based on a stylized model
15 km one hour morning peak
CBD
Suburb
Base case buses delayed by congestion
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Measures
Discreet policy measures tested (with optimised
policy variables).
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(No Transcript)
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Prelimenaries PT supply and pricing

• Public transport is a service
• From the users standpoint the quality of the
  service depends on several factors- The density
  of the routes (influences walking distances to
  and from stops.- Frequency of services
  (influences waiting times and scheduling of
  activities).- Crowding (influences comfort)-
  Design of the system (influences the number of
  transfers etc).- Hours of operation per day-
  Running speed (influences onboard time)
• General quality is difficult to measure due to
  the many dimensions and we must usually simplify
  to measurable dimensions.
• CBA can be used for specific changes in supply,
  i.e. a new urban rail line, a new bus route, but
  we usually need a comprehensive transport model
  to evaluate the impacts on demand.

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Improved quality of a service shifts the demand
curve
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Number of revenue kilometres operated per hour
a proxy for some aspects of quality (waiting,
walking and possibly transfers).

• Empirical work (econometric studies) have shown
  that revenue kilometres operated per unit of time
  affects demand. Elasticity of demand lt 0.3 0.6
  gt.
• CBA analysis of a general improvement in the
  quality of PT-services assuming that additional
  revenue kilometres are added in the most
  efficient way.

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Structure of urban PT-service
Add. peak service
Basic service
Lower cost per rev.km for basic service than for
additional peak service
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3 categories of demand

• The peak demand that dimensions the total
  capacity (number of vehicles, drivers etc).
• Other peak demand
• Off-peak demand

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Questions?

• How should we determine rev. kms per hour for
  basic services and additional peak services?
• What is the appropriate fare structure?
• What is the appropriate capacity per revenue
  kilometre?
• Objective Maximize social surplus of the
  PT-system.
• Additional concerns- Sufficient capacity- Cost
  of public funds- Transfer of car drivers and
  external costs of car use

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Model of PT-system i Oslo

• 3 demand functions, one for each category of
  demand.
• Cost function depending capital and operating
  cost per vehicle for peak and basic services and
  fixed cost per passenger (ticketing) and capacity
  of vehicles.
• Consumer surplus for PT-users
• Benefits of transferring car drivers
• Constraints on utilization of capacity
• Cost of public funds

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Maximising social surplus - no concern for car
traffic
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Maximising social surplus - accounting for
impacts on car traffic
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2. Best gt congestion and no congestion pricing