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

TL;DR

This paper presents a systematic, open-source methodology for creating a synthetic population for activity-based transportation modeling using publicly available data, demonstrated through a case study in Tallinn, Estonia.

Contribution

It introduces a novel, efficient approach for spatially assigning synthetic populations from aggregate data, facilitating activity-based demand modeling.

Findings

Developed a reproducible methodology for synthetic population assignment.

Created an open-source dataset for Tallinn's transportation modeling.

Demonstrated the approach's effectiveness in a real-world case study.

Abstract

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.