Assignment of a Synthetic Population for Activity-based Modelling employing Publicly Available Data

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.