  Agent based modelling has acquired the spotlight in the transportation domain
both in scientific literature and in real life applications, thanks to its
capability to deal with the ever-growing complexity of transportation systems,
including future disrupting mobility technologies and services such as
automated driving, Mobility as a Service, and micromobility. Different software
emerged, dedicated to the simulation of disaggregate travel demand framing
individual choices based on the profile of each agent. Still, the actual
research work exploiting these models is scarce and the professionals with the
knowledge to use them are few. This may be ascribed to the large amount of
needed input data or to a lack of commercial solutions and of research
production detailing the process leading to the actual simulations. In this
paper, a methodology to spatially assign a synthetic population by exploiting
publicly available aggregate data is presented and implemented on a case study.
In doing so, the paper provides a systematic approach for a quick and efficient
treatment of the data needed for activity-based demand generation. Finally, the
obtained dataset, representing a synthetic population of the city of Tallinn,
Estonia, and its spatial assignment, is described so that it may be exploited
by fellow researchers, since both the tools needed for spatial assignment and
the resulting dataset are made available as open source.
