LUNA - Brief description
The main scope of the model LUNA is to assess long term effects of changes in socio-demography, economy, technology and transport policy. Hence the time horizon of the model is 2050. LUNA is based on the principles of Systems Dynamics and was programmed utilising the System Dynamics software Vensim(r). Microsoft Excel(r) is used as data interface. LUNA currently covers the EU27 member states plus Norway and Switzerland. LUNA takes the trip purposes vacation and business into account. The model can be subdivided into the following sub-modules:
- a population cohort model,
- a household formation model,
- a car ownership model,
- a non-OD-matrix based transport demand model,
- an aggregate transport supply model and
- an evaluation indicator module.
The population is subdivided into 18 age groups in five year time steps from 0-4 years up to 85 and more years. The LUNA household formation sub-model assigns the population of the different age groups to the different household types and income groups. Household types have been defined corresponding to the Eurostat database (single person household 20 to 59 years, two persons household 20 to 59 years, family household with one child, family household with two children, family household with three or more children, single parent HH with children, three or more adults, three or more adults with children, two persons household 60 years and older and single person household 60 years and older). The development of regional household income is calculated from the scenario assumptions concerning regional GDP development.
The LUNA transport demand model consists of the following sub-models:
- a car availability model,
- a trip rate model, and
- a distance class and mode choice model.
The following five different modes of transport are available in LUNA: private car, bus and coach, railway, air and maritime. Regional car availability of households is calculated from household type and income. Trip rates are a function of household income and car availability. The distance class and mode choice sub-model uses so called friction factors to distribute the total demand for vacation trips to the different distance bands and modes of transport. Friction factors are indicators to measure the subjectively perceived effort in terms of time and money which is necessary to carry out a journey. Friction factors can be interpreted as a kind of generalised costs although measured in time rather than money. The concept of friction factors with subjective weighting factors for different parts of a journey stems from (Walther, K., et al. 1997).
A detailed description of modelling principles and results can be found in Deliverable 3.1 and 7.1 of ORIGAMI (Link).
Walther, K., Oetting, A. and Vallée, D. (1997) Simultane Modellstruktur für die Personenverkehrsplanung auf der Basis eines neuen Verkehrswiderstands, Aachen.