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Main features

Strategic evaluation of REVENUES : the structure of MOLINO KUL & adpC JUNE 8-10 REVENUE MEETING LEUVEN, KUL. Main features. Individual trips: private (or public) transportation Freight transportation Simplistic network. INPUT 1: data. Demand systems Preferences / Objectives

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Main features

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  1. Strategic evaluation of REVENUES :the structure of MOLINOKUL & adpCJUNE 8-10 REVENUE MEETINGLEUVEN, KUL

  2. Main features • Individual trips: private (or public) transportation • Freight transportation • Simplistic network

  3. INPUT 1: data • Demand systems • Preferences / Objectives • Users  parameterized utility function • Firms (freight)  parameterized production function • Construction costs function F(K) • Infrastructure capacity: K • User or congestion cost function C(N,K)

  4. INPUT 2: Policies • Different regimes: • No toll regime • First-best optimum (welfare optimization) • Second-best optimum toll (e.g. Ramsey) • Competitive market (profit optimization) • Mixed regime (profit /welfare optimization) • Capacity: construction & maintenance

  5. Segmentation • Horizontal segmentation (spatial; network) • Vertical differentiation (incomes) • Temporal differentiation (peak and off peak) • Country specifics (local or transit freight transport)

  6. ENGINE • Wardrop equilibrium (with congestion) • Nash equilibrium • Optimal capacity

  7. OUTPUT • Toll levels • Revenues • User generalized cost • Congestion level and flows • Welfare (overall and per group) • Capacity • Optimal level • Depreciation

  8. Dynamics • Year to year adjustments process (myopic of perfect optimization) • Stock variable: capacity • Flow variable: revenues • Driving forces: • Variable passenger and freight demand level • Shift in parameters (income, etc.) • Policy change (market structure & capacity)

  9. Remains to be done • Test the overall structure • More general setting • Network structure • Segmentation • Regimes • Interface for input and output (e.g. Excel) • Automatic calibration • Dynamic deterministic (stochastic) optimization

  10. Mile stones • Model the case study in a framework suitable for the MOLINO • Input data (static and dynamic) • Data for calibration • Identify policies to be tested

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