MOLINO II -model structure-

MOLINO II -model structure-

KULeuven and ADPC

Contents

• MOLINO I:– Overview– list of improvements needed

• MOLINO II:– Network structure and definitions– Economic agents and their behaviour– Financial module– Software– Uncertainty– Link with corridor models

Objectives of MOLINO I

• Small model to support implementation of theoretical guidelines of Revenue-consortium

• Designed to compute impacts (short to long term) of alternative pricing, investment and revenue use strategies

• Implementable for all case studies

Data (t = 1,…,T)

•Calibration data for transport demand and behaviour•Cost data for operation and maintenance•Initial infrastructure stock (t =0)•Initial financial structure (t =0)

Policy InputsRegulation sheme (t = 1,…,T)

•Pricing rules•Investment rules•Revenue use rules•Types of contracts

MOLINO ITransport market module + financial + investment

module running from t = 1…T

Outcomes

•Transport flows•Prices, capacity•Welfare, revenue, equity•Financial structure

Key Features of MOLINO I

Realisation MOLINO I

Dimensions of model:– Any 2 competing modes allowing for exogenous

pricing rules, profit max (Nash) pricing or welfare maximising pricing

– Investments exogenous– 2 types of passenger transport (poor, rich) and 2

types of freight transport (local, transit)– Role for operator and infrastructure manager– Simple dynamics of infrastructure fund– Reduced form coefficients for contract efficiency,

marginal cost of funds, equity effects

Network in MOLINO I

Imperfect substitution

passenger richpassenger poor

transit freightnational freight mode 2

mode 1

MOLINO I improvements needed

• Network: from simple parallel network to • Serial network + parallel network+ combinations• More than 2 alternatives in parallel network (road, rail, air or 2 roads and rail etc.)

• More types of users• More flexible congestion functions (MOLINO I-linear)• Improved financial module• Uncertainty: demand and costs• Dynamics (perfect foresight ??)• Software: now Mathematica

– Interface with users– Portable software

Contents



Network representation

• The final objective of the model is to study a particular infrastructure investment project.

• The procedure is to start with the investment project and its corridor– This means a physical network generally implying

only one mode – The network will be defined using OD pairs, links and

paths

Madrid

Montpellier

Bordeaux

Liboa

MA

MO

BO

LI

slow train

TGVTGV

Example(deliberately slightly different from TEN project: build TGV between Barcelona and Madrid)

Before After

Need description of actual situation and situation with investment

BarcelonaBA

Step1: Define OD pairs:

MO-BAMO-MABA-MABO-MAMO-LIBA-LIBO-LIMA-LI

MA

MO

BO

LI

Network representation: example

BA

Step 1 OD pairs

Step 2 add links:

Rail links: T1,T2,T3,T4Road links: R1,R2,R3,R4,R5, R6(R1 R6, R1 R2)Air links: A1,A2,A3,A4,A5BO

MA

MO

LI

T1

T2

T3

T4

R1

A1

A2

A5

R2

R3

R5

R4

A4

A3

R6

Network representation : example

Step 1: OD pairs

Step 2: add links of potentially competing routes or modes

Step 3: Define paths, combining links that bring you from O to DPath is defined Px(link1,link2, ..)

Legend Figure:Black= rail RxGreen= air AxBlue= road Rx


MA

MO

LI

T1

T2

T3

T4

R1

A1

A2

A5

R2

R3

R5

R4

A4

A3

R6

BO

• Examples for OD, MO-LI:

P1(A1), P2 (R2),P3(R1,R3) P4(R1,A5),P5 (R1,T3),P6(T1,T4,T3), P7(T1,T4,R3), P8(T1,T4,A5),P9(A2,T3), P10 (A2,R3),P11 (A2, A5),P12(T1,R6,T3), P13(T1,R6,R3), P14(T1, R6,A5)

P12(A6) – corridor models ???

• Examples for OD, MO-MA:

P1(T1,T4), P2(A2), P3(R1), P4(T1,R6)

Network representation : exampleParis

MA

MO

LI

T1

T3

T4

R1

A1

A2

A5

R2

R3

A6??

R6

For each path we define the links that constitute the path – links may be part of different paths so links will receive different types of users having different destinations

R1 … R6 T1 … T4 A1 A2 … A5

MO-MA P1 X X

MO-MA P2 X

MO-MA P3 X

MO-MA P4 X X

MO-LI P1 X

….

MO-LI P11 X X

….


For each link we define capacity,

maintenance cost functions, investment costs, speed

flow functions etc.

speed length Capacity Maintenance

costs

Investment

costs

R1

….

A5


Network representation

• Serial links added (n but not too large, some TEN projects have 15 segments..)

• Parallel links: n choices offered

• But in modelling:

“Less can be more”

smaller number of alternatives can often generate a lot more insights…

Contents



• Users: different types

• Operators of rail services/roads/air

• Infrastructure owners

• Governments: set taxes and are concerned about consumer surplus of some of the users and some of the profits

4 categories of Economic Agents

• We define different users for each OD pair considered:

Types of users (data available?):– Passengers: business, leisure, commuting? – Freight: general cargo, container, bulk

• Every type of user has its own preferences

• Distinction of users is important to represent benefits of projects (values of time etc.), for equity issues but also financial revenue potential depends on this distinction (price discrimination)

Economic Agents: Users (1)

For each type of user and OD we define preferences: e.g. (leisure) Passenger for MO-LI (nested CES)

Utility

Transport Other consumption

P11…

Peak

P1 P2 P6… P11…

Off-Peak

P1 P2 P6…

Economic Agents: users (2)

At lowest level one needs quantities and generalized prices for each path.+ elasticities of subst between paths (remember a path also represents modes)

σ1

σ2

σ3a σ3b

More than 2 choice options calls for nesting in order to better represent substitution???

Road?

P1 P2 Pk…

Utility

Transport Other consumption

Peak Off-Peak

Non-Road?

P(k+1) Pn…

Road?

P1 P2 Pk…

Non-Road?

P(k+1) Pn…

σ4a σ4b

σ3a

User preference representation

• CES utility or CES cost tree with max 4 levels for each OD pair

• Number of alternatives can change over time when investment adds an option (to be checked)

User cost

VOT Resource

Cost

Tolls , taxes etc.

MO-MA

Type1(leisure)

….

type k (business)

….

BO-LI

For each OD there are different types of users. For every type of user specify user cost

• Users: different types

• Operators of rail services/roads/air

• Infrastructure owners

• Governments: set taxes and are concerned about consumer surplus of some of the users and some of the profits

4 categories of Economic Agents

• Difference in objective functions between different private and public agents

• Private agents: max profits of their own link – Option: can they also operate several links and max over different

segments?

• Public agents– Local governments: max welfare of local users only (only some

OD pairs) + own net tax revenue– National or EU governments: max welfare more globally

Economic Agents: operators & infrastructure manager (1)

• For each link we specify who operates/manages

Operator Infrastructure

manager

Tax collector

R1 Private /

Local govt /

Central govt…

Gov A….

….

A5


• For each link we specify type of contracts

tendering: YES/NO?

Maintenance Investment Operation

R1 Y N N

…. Y

A5


• Operators:

orj = Toll revenuesj – INFCj

– θor,j (Operation costs)j+ subjor

• Infrastructure managers:

infj = INFCj – θmc,j (Maintenance costs)j

– θinv,j (Investment costs)j + subjinf

• θor,j , θmc,j , θinv,j : tendering parameters.

→ depends on type of contracts


Contents


• MOLINO II:– Network structure and definitions– Economic agents and their behaviour– Financial module– Software – Uncertainty– Link with corridor models

Financial Report Module

Final user

Competitive Supplier

OperatorTransport services

Centralgovernt

Local governt

Infrastructure manager

Localtax

Federaltax

Resource costs

Tolls, charges, tickets

Infrastructure use charge

Maintenanceand

Investments

operation

profit tax

profit tax

subsidy

tax or subsidy

Infrastructure Fund

Final user

Competitive Supplier

OperatorTransport services

Centralgovernt

Local governt

Infrastructure manager

Localtax

Federaltax

Resource costs

Tolls, charges, tickets

Infrastructure use charge

Infrastructure Fund

tax or subsidy

subsidy

Contents


• MOLINO II:– Network structure and definitions– Economic agents and their behaviour– Financial module– Software (José)– Uncertainty (Stef)– Link with corridor models (Stef)

Demand Uncertainty (1)

• Different types of uncertainty– Demand– Costs– Model parameters

• Demand substitutability, congestion costs, etc.

Demand Uncertainty (2)• Methodology

– Short cuts to introduce uncertainty• Higher cost of capital: poor procedure ??• For Demand uncertainty in presence of

congestion: – Develop another investment rule: “invest more than

optimal investment for expected demand level”

– Monte Carlo analysis• Simulate statistical distribution results by drawing

from the distribution of uncertain parameters• Stochastic programming: optimise investment

strategy given progressive learning over time

Documents

MOLINO II -model structure-