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 Inputs Regulation sheme (t = 1,…,T) Pricing rules Investment rules Revenue use rules Types of contracts MOLINO I Transport 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 rich passenger poor transit freight national 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 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

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 TGV Example (deliberately slightly different from TEN project: build TGV between Barcelona and Madrid) Before After Need description of actual situation and situation with investment Barcelona BA

Step1: Define OD pairs: MO-BA MO-MA BA-MA BO-MA MO-LI BA-LI BO-LI MA-LI MA MO BO LI Network representation: example BA

Step 1 OD pairs Step 2 add links: Rail links: T1,T2,T3,T4 Road links: R1,R2,R3,R4,R5, R6 (R1 R6, R1 R2) Air links: A1,A2,A3,A4,A5 BO 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 D Path is defined Px(link1,link2,..) Legend Figure: Black= rail Rx Green= air Ax Blue= road Rx Network representation : example 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 : example Paris 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…R6T1…T4A1A2…A5 MO-MA P1XX MO-MA P2X MO-MA P3X MO-MA P4XX MO-LI P1X …. MO-LI P11XX …. Network representation : example

For each link we define capacity, maintenance cost functions, investment costs, speed flow functions etc. speedlengthCapacityMaintenance costs Investment costs R1 …. A5 Network representation : example

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 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

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 P1P2P6 … P11 … Off-Peak P1P2P6 … 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) σ1σ1 σ2σ2 σ3aσ3b

More than 2 choice options calls for nesting in order to better represent substitution??? Road? P1P2Pk … Utility Transport Other consumption PeakOff-Peak Non-Road? P(k+1)Pn … Road? P1P2Pk … 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 VOTResource 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 OperatorInfrastructure manager Tax collector R1 Private / Local govt / Central govt… Gov A…. …. A5 Economic Agents: operators & infrastructure manager (2)

For each link we specify type of contracts tendering: YES/NO? MaintenanceInvestmentOperation R1 YNN …. Y A5 Economic Agents: operators & infrastructure manager (3)

Operators:  or j = Toll revenues j – INFC j – θ or,j (Operation costs ) j + sub j or Infrastructure managers:  inf j = INFC j – θ mc,j ( Maintenance costs ) j – θ inv,j ( Investment costs ) j + sub j inf θ or,j, θ mc,j, θ inv,j : tendering parameters. → depends on type of contracts Economic Agents: operators & infrastructure manager (4)

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

Financial Report Module Final user Competitive Supplier Operator Transport services Central governt Local governt Infrastructure manager Local tax Federal tax Resource costs Tolls, charges, tickets Infrastructure use charge Maintenance and Investments operation profit tax

subsidy tax or subsidy Infrastructure Fund Final user Competitive Supplier Operator Transport services Central governt Local governt Infrastructure manager Local tax Federal tax Resource costs Tolls, charges, tickets Infrastructure use charge Infrastructure Fund tax or subsidy

Contents MOLINO I: –Overview –list of improvements needed 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