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How to use?

In the following, we describe the purposeful usage of the model.

Preliminary knowledge for usage

Given the complex nature of transport systems that is highly variable in different spatial environments and geography, the user should have some experience with the formulation and application of optimization models. In preparation to the application, we urge to familiarize yourself with the content of the Model design chapter. A mathematical formulation of the model can be found in the repository in mathematical_formulation-equations/math_formulation.pdf.

Design of a case study

In designing and quantifying a case study for the transport component model, the following questions need to be addressed:

  • What region is modeled and at which geographic resolution? The model has been previously applied at NUTS-2 and NUTS-3 level (more information on NUTS classification here).
  • What temporal horizon is modelled? The suggested modeling horizon is at least five years to find implications for modal and technological shift.
  • Which transport modes, drivetrain technologies and fuels are part of the analysis?
  • How granular are the modes modeled? Based on vehicle stock investments or using levelized costs representing the total costs of a mode? This decision depends on the desired granularity of the analysis as well as available data.

Preparing input data

The input data is in YAML format. The minimal required input data and its format is defined in Input data. This is read using get_input_data and the data is checked and parsed using parse_data.

Model application

The case study complexity may vary depending on which constraints are applied to the model.

Minimum viable case - Demand coverage

Applied functions:

  • create_model
  • constraint_demand_coverage
  • objective

Output: Cost-optimal coverage of travel demand by year, odpair, mode, vehicle technology, generation and paths.

Vehicle stock sizing

Applied functions:

  • create_model
  • constraint_demand_coverage
  • constraint_vehicle_sizing
  • objective

Output: Cost-optimal coverage of travel demand with sizing of required vehicle stock.

Vehicle stock aging

Applied functions:

  • create_model
  • constraint_demand_coverage
  • constraint_vehicle_sizing
  • constraint_vehicle_aging
  • objective

Output: Cost-optimal coverage of travel demand with sizing of required vehicle stock under the consideration of the age structure of the vehicles

Constrained technology shift

Applied functions:

  • create_model
  • constraint_demand_coverage
  • constraint_vehicle_sizing
  • constraint_vehicle_aging
  • constraint_vehicle_stock_shift
  • objective

Output: Cost-optimal coverage of travel demand with sizing of required vehicle stock under the consideration of the age structure of the vehicles and limitations on the speed of vehicle stock shift

Fueling infrastructure sizing

Applied functions:

  • create_model
  • constraint_demand_coverage
  • constraint_vehicle_sizing
  • constraint_fueling_demand
  • objective

Output: Cost-optimal coverage of travel demand with sizing of required vehicle stock and expansion of fueling infrastructure

Constrained mode shift

Applied functions:

  • create_model
  • constraint_demand_coverage
  • constraint_vehicle_sizing
  • constraint_mode_shift
  • objective

Output: Cost-optimal coverage of travel demand with sizing of required vehicle stock and constrained mode shift

Mode infrastructure sizing

Applied functions:

  • create_model
  • constraint_demand_coverage
  • constraint_mode_shift
  • objective

Output: Cost-optimal coverage of travel demand under limitation of speed of shift

Save your results with save_results.