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Basque case study

Case study description

  • The case study encompasses the federal state Basque Community in Spain. This consists of three subregions which is the spatial extent of the case study, considering the trips within the regions and between the three regions. The temporal horizon is 2020-2050 with considering vehicles bought since 1995.
  • Five income levels are considered among with also commercial trips. These are called First quintile, Second quintile, Third quintile, Fourth quintile, Fifth quintile and Commercial in the input data which are defined under FinancialStatus and for all corresponding trips in Odpairs. The First quintile is the consumer group with lowest available budget, while the Fifth quintile the one with the highest.
  • Also purposes
  • Considered modes are road and public transport.
  • Vehicle types: small passenger cars, medium passenger cars, SUVs and busses.
  • considered technologies are internal comubustion engine (ICE) and battery-electric (BE)
  • Fuels: diesel and electricity
  • For the trip data, a data set is used which contains origin-destination trip data classified by mode and purpose. For the case study, the classification by purpose is introduced by considering different average path lengths.

Description of the input data

The input file for this case studdy can be found unter examples\Basque country\data\basque_country_input.yaml. The data input is ordered alphabetically after the key name.

  • GeographicElement describes all geographic features of the case study, together with the local carbon pricing development which is assumed here to be constant 60 euros/tCO2. The naming of the geographic features follows the NUTS-3 classification for the European Union.
  • Mode, Technology, Vehicletype, Fuel and TechVehicle desribe the portfolio for the supply of transport services. The technical developments of the drive-train technologies are included in the key TechVehicle, where technical aspects are given for the all generations of vehicles (Important: A generation is the year in which the vehicle type of a certain technology is released on the market.). As maintenance costs increase depending on the vehicle's age. Therefore, the parameters related to this are two-dimensional lists and defined for Generation $\times$ Lifetime.
  • Odpair, Regiontype, FinancialStatus, Product and Path encompass all information of the demand side. Odpair displays trip magnitudes to be covered, while Path the geographic allocation of possible travel paths between origin and destination. The consumer resources and consumer-specific characteristics which are not trip-related are in FinancialStatus. Next to the monetary budget and the definition of the purchase horizon which relates to the frequency of the purchase of a new vehicle, the Value of Time (VoT) is an important parameter. It assigns a monetary value to the time spend on travel.
  • InitialVehicleStock, InitialFuelingInfr and InitialModeInfr comprise the information of the initial values on infrastructure capacities and vehicle stock for the case study.

Data sources

Data sources used for transport modes, and cost and performance parameters:

  • VoT values are deduced from the publication [1].
  • ETISplus [2]: Within the project ETISplus, origin-destination data was calibrated at NUTS-3 resolution for all modes and both, passenger and freight transport. The data specifies also trip pruposes.
  • Grube et al., 2021 [3]: The authors provide a cost comparison between different kinds of vehicle types of passenger cars and drivetrain technologies.

References: [1] Tattini, J., Ramea, K., Gargiulo, M., Yang, C., Mulholland, E., Yeh, S., & Karlsson, K. (2018). Improving the representation of modal choice into bottom-up optimization energy system models – The MoCho-TIMES model. Applied Energy, 212, 265–282. https://doi.org/10.1016/j.apenergy.2017.12.050

[2] Szimba, E., Kraft, M., Ihrig, J., Schimke, A., Schnell, O., Kawabata, Y., Newton, S., Breemersch, T., Versteegh, R., Meijeren, J., Jin-Xue, H., de Stasio, C., & Fermi, F. (2012). ETISplus Database Content and Methodology. https://doi.org/10.13140/RG.2.2.16768.25605

[3] Grube, T., Kraus, S., Reul, J., & Stolten, D. (2021). Passenger car cost development through 2050. Transportation Research Part D: Transport and Environment, 101, 103110. https://doi.org/10.1016/j.trd.2021.103110

Output data