MOLINO II -model structure-

1. MOLINO II -model structure- KULeuven and ADPC

2. 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

3. 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

4. Key Features of MOLINO I • 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

5. 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

6. Network in MOLINO I passenger rich passenger poor transit freight national freight mode 1 mode 2 Imperfect substitution

7. 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

8. 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

9. 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

10. Example (deliberately slightly different from TEN project: build TGV between Barcelona and Madrid) Before After Montpellier MO TGV TGV slow train BA Barcelona Madrid MA Bordeaux BO Liboa LI Need description of actual situation and situation with investment

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

12. A1 MO R2 R1 T1 R6 T4 A2 MA A3 T2 R4 T3 A5 R3 R5 LI A4 Network representation : example 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

13. A1 MO R2 R1 T1 R6 T4 A2 MA A3 T2 R4 T3 A5 R3 R5 LI A4 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 BO

14. Paris A6?? A1 MO R2 R1 T1 R6 T4 A2 MA T3 A5 R3 LI Network representation : example • 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)

15. 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 Network representation : example

16. Network representation : example For each link we define capacity, maintenance cost functions, investment costs, speed flow functions etc.

17. 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…

18. 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

19. 4 categories of Economic Agents • 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

20. Economic Agents: Users (1) • 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)

21. Utility Transport Other consumption Peak Off-Peak P1 P2 P6 … P11 … P1 P2 P6 … P11 … Economic Agents: users (2) For each type of user and OD we define preferences: e.g. (leisure) Passenger for MO-LI (nested CES) σ1 σ2 σ3a σ3b At lowest level one needs quantities and generalized prices for each path. + elasticities of subst between paths (remember a path also represents modes)

22. Road? P1 P2 Pk … More than 2 choice options calls for nesting in order to better represent substitution??? Utility Transport Other consumption Peak Off-Peak σ3a Road? Non-Road? Non-Road? σ4a σ4b P1 P2 Pk P(k+1) Pn P(k+1) Pn … … …

23. 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)

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

25. 4 categories of Economic Agents • 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

26. Economic Agents: operators & infrastructure manager (1) • 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

27. Economic Agents: operators & infrastructure manager (2) • For each link we specify who operates/manages

28. Economic Agents: operators & infrastructure manager (3) • For each link we specify type of contracts tendering: YES/NO?

29. Economic Agents: operators & infrastructure manager (4) • Operators: porj = Toll revenuesj – INFCj – θor,j (Operation costs)j+ subjor • Infrastructure managers: pinfj = 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

30. 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

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

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

33. 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)

34. Demand Uncertainty (1) • Different types of uncertainty • Demand • Costs • Model parameters • Demand substitutability, congestion costs, etc.

35. 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