economics of road network ownership

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Economics of Road Network Ownership

1st International Conference on Transport Infrastructure FundingBanff, Canada, August 2-3, 2006

Lei Zhang

Assistant ProfessorDept. of Civil, Construction,&Environmental EngineeringOregon State [email protected]

David Levinson

Associate ProfessorDepartment of Civil EngineeringUniversity of [email protected]

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Road Network Ownership

Nationalization Privatization

Decentralization

Centralization

Competitive Market

Centralized Government

Local/Municipal Government

PrivateMonopoly

U.S. 2006

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Questions about Network Ownership

For a specific road network…

What is the optimal ownership structure given certain

demand characteristics and cost functions?

Whether/How should ownership changes take place?

Practical Implications

Private toll roads

Public private partnership/competition

Regulation on price, investment, and ownership

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Research Objectives

Welfare consequences of …

Centralized public ownership

Decentralized private ownership (market-oriented)

Implications of alternative ownership on

Prices (tax, toll)

Network capacity

Regulatory needs and policies

Methodological focus

Quantitative modeling

Equilibrium (point) and evolutionary (process) analyses

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Method

Optimization

and

Simulation

Time

Year t-1 Year t Year t+1

EXOGENOUS VARIABLES Land use, Demographics, Socio-Economic Changes

Behavior: Travel Demand Estimation Zone-based, Gravity distribution, Single Mode, User equilibrium traffic assignment

Time

Technology: Maintenance Cost Construction Cost Revenue

Network t

Flow Flow Toll/Tax

MEASURES OF EFFECTIVENESS Mobility, Accessibility, Social Welfare, Financial Indicators

NETWORK MODEL

Policy: Pricing Policy Investment Policy Ownership

Network t+1

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Centralized Ownership

Pricing Policy

Fuel taxes, Registration fees, Distance-based tolls

= Average cost pricing

Investment Strategy

Construction budget = Revenue – Maintenance Cost

Priority: Links with the highest benefit/cost ratios

Solve

for each link

At the end of each fiscal year: Budget = Expenditure

)( CapacityBCRatioMaximizeCapacity

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Decentralized Ownership: Pricing

A Dynamic Pricing Game among All Roads

Uncertainty and incomplete information

Profit-Maximizing Pricing through Adaptive Learning

1. Estimate a demand curve

based on (price, flow) data

in previous years for each link;

2. Solve Maximize Profit(price);

3. Case A: PLow < Price* < PHigh

New Price = Price*

Case B: Price*<PLow or > PHigh

New Price = PLow(1 – j)

or = PHigh(1 + j)

A

B

Price: Toll + Monetized Travel Time

Demand: Flow

P*B PB PLow P*A= PA PHigh

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Decentralized: Investment

Rate of Return

(Lifecycle Revenue – Lifecycle Cost)/Capital Cost

Links can borrow or earn interest through a “Bank” agent

If Rate of return > interest rate Borrow & Build capacity

Otherwise Pay off loan or Save

Profit Estimation for Capacity Expansion

Consider two sources of additional profit after expansion

1. Ability to charge higher tolls

2. Ability to attract more users

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Fixed Demand: Equilibrium AnalysisEquilibrium Capacity on a 10by10 Grid Network

Decentralized Ownership

Socially Optimal


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Fixed Demand: Equilibrium Analysis 2

Equilibrium Toll

Decentralized Ownership

Socially Optimal


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Decentralized Ownership with Regulation

0

5

10

15

20

25

$1 $2 $3 $4 $5 $6 $7 $8 $9 Ceiling Price

Net Social Benefits

Consumers' Surplus

Profit

Socailly Optimal

Million $

Determination of the Optimal Ceiling Price

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Fixed Demand: Evolutionary Analysis

0

5

10

15

20

25

0 10 20 30 40 50 60 70 80 90 Year

Socially OptimalDecentralized: Profit-MaximizingCentralized: Average Cost PricingDecentralized: Price Ceiling

Net Social Benefit (M$)

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Fixed Demand: Evolutionary Analysis 2

0

2

4

6

8

10

0 10 20 30 40 50 60 70 80 90Year


Average Toll ($)

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Fixed Demand: Evolutionary Analysis 3

0

100

200

300

400

500

0 10 20 30 40 50 60 70 80 90Year


Cumulative Number of Capacity Expansion Projects

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Variable Demand: Evolutionary Analysis

0

40

80

120

160

200

0 10 20 30 40 50 60 70 80 90 Year

Socially Optimal

Decentralized: Profit-Maximizing

Centralized: Average cost pricing

Net Social Benefit (M$)

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Variable Demand: Evolutionary Analysis 2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 10 20 30 40 50 60 70 80 90 Year

Socially Optimal



Average Toll ($)

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Variable Demand: Evolutionary Analysis 3

0

200

400

600

800

1000

1200

1400

0 10 20 30 40 50 60 70 80 90 Year

Socially Optimal



Cumulative Number of Capacity Expansion Projects

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Conclusions

Nothing is perfect

Pros Cons

Centralized Status quo Low tolls, Ineffective

Cost recovery Sub-optimal capacity

Decentralized Responsiveness High tolls

Market-oriented Risk of over-investment

When appropriate regulation is imposed (e.g. Pmax)

and/or travel demand is steadily increasing (e.g. 3%)

Results are in favor of decentralized market-oriented approach

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Future Studies

Analysis on real-world networks (Portland, OR, Twin Cities, MN)

Consideration of hybrid ownership and ownership dynamics

Assessment of more sophisticated regulatory policies

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Thank you!

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Detailed Flowchart

TIME

Year t-1 Year t Year t+1

Exogenous land use, demographic, economic changes

Trip Generation

Trip Distribution

Zone production and attraction totals

UE Traffic Assignment

OD demand; k = 0

TIME

Revenue Model

Link flow k

OD cost table t + 1 OD cost table t

Cost Model

Revenue

Investment Model

Network t Network t + 1

Pricing Model

New capacity and free-flow speed

flow k = flow k–1 ?

Yes

No

k = k + 1 Link toll k

Flow, toll, travel time, OD cost

Maintenance cost Construction cost

Measures of Network Effectiveness: VHT, VKT, CS, Revenue, etc.

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Notes:

Supply and demand do not have to be solved simultaneously; An agent-based travel forecasting system is desirable; Incremental/Adaptive changes in travel behavior are important.

Trip generation and distribution

Zone-based structure Doubly-constraint gravity model

Traffic assignment

Origin-based User Equilibrium Assignment (Bar-Gera and Boyce 2003) Generalized link travel cost function

VOT•BPR travel time + Toll

Travel Demand

)( 1 irsssrr

irs tdDnOmq

iai

a

ia

ia

aia F

fv

lt

2

11

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Revenue and cost functions

Revenue A notion of link revenue is convenient in describing various policies

Revenue Toll • Flow

)( ia

ia

ia fE

Cost

Only a portion of maintenance cost is volume-dependent (Paterson and Archondo-Callo 1991) link maintenance cost (M) function for all links:

Empirical studies suggest (Karamalaputi and Levinson 2003) link expansion cost depends on link length, capacity and capacity change

Link expansion cost (K) function:

Length Capacity Flow

321 )()()( ia

iaa

ia fFlM

321 )()()( 1 ia

ia

iaa

ia FFFlK

Length Capacity Capacity Change

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Model Parameters

A complete set of parameters derived for the Twin Cities

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The agent-based simulation model of network growth seems to be a promising new approach to analyze pricing, investment strategies, and ownership structures because:

It considers both short- and long-run network dynamics;

It can evaluate alternative sub-optimal policies;

It can be easily applied to large-scale real-world networks without loss of details or computational hurdles;

It can incorporate results from studies of organizational behavior and ownership arrangements without additional modeling efforts;

Model Capabilities

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Test Network

32 kmInitial conditions

Uniform land use with one million total trips

All links are one-lane with capacity 735 veh/hr

A very congested network (average speed 10 km/h)

Homogenous users

economics of road network ownership

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