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

main features
Main features
  • Individual trips: private (or public) transportation
  • Freight transportation
  • Simplistic network
input 1 data
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)
input 2 policies
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
segmentation
Segmentation
  • Horizontal segmentation (spatial; network)
  • Vertical differentiation (incomes)
  • Temporal differentiation (peak and off peak)
  • Country specifics (local or transit freight transport)
engine
ENGINE
  • Wardrop equilibrium (with congestion)
  • Nash equilibrium
  • Optimal capacity
output
OUTPUT
  • Toll levels
  • Revenues
  • User generalized cost
  • Congestion level and flows
  • Welfare (overall and per group)
  • Capacity
    • Optimal level
    • Depreciation
dynamics
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)
remains to be done
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
mile stones
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