ANTMOD stands for „Agile National Transport Model“ and constitutes an agent-based demand model for passenger transport. The model is based on the largest national travel survey in Germany (“Mobilität in Deutschland 2017”, in short: MiD) and is implemented in the programming language R. It is characterized by fast computing times, convenient handling, and high flexibility. Hence it is particularly well suited for the analysis of new developments and trends as it allows for the quick and easy implementation of additional impact factors.
The model was developed on the basis of the macroscopic four-step travel model and includes the stages of trip generation, mode choice, and destination choice. It is highly detailed on the level of the individual agents and distinguishes among person, household, and mobility attributes such as sex, age, income, and car ownership. In particular, this information is used in the stages of trip generation and mode choice. The latter is computed in an iterative loop together with the infrastructure capacity utilisation and the generalised costs for each mode of transport. Hence the model includes both specific attributes of the different modes of transport such as the access and egress time as well as more general parameters such as traffic flow.
Overview of the model ANTMOD (Agile National Transport Model)
ANTMOD models a detailed travel distance choice for each trip depending on the spatial area in which the trip is conducted instead of a spatial modelling of the route assignment on the basis of origins-destination matrices. The spatial area categories are based on the classifications of the Federal Ministry of Transport and Digital Infrastructure developed for mobility and transport research and regional statistics which distinguish among urban, suburban, and rural area type. Refraining from a detailed spatial modelling of the route assignment provides the advantages of considerable improvements in computation time.
This enables the model to produce key indicators in transport research in a relatively fast manner. This includes, for example, the annual mileage for each means of transportation in the three spatial categories and the modal split for different trip purposes. In addition, impact assessments of different policy measures such as the introduction of a fare-free public transport of the increase of energy taxes can be developed relatively fast.