Abstract
Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.
Highlights
Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers
The work in [15] adds land use variables in the generation of synthetic population and the results suggest that the addition of such variables improves the capability to frame additional nuances, such as, e.g., the differences in mobility patterns between rural and urban areas
The output of this step is the synthetic population integrated with anchor points, input for activity-based demand generation
Summary
A similar picture appears in Europe, with a congestion cost estimated at approximately EUR 110 billion per year, in terms of delay [2], while urban mobility accounts for 40% of all CO2 emissions from road transport [3] This is expected to become more complex in the following decades, with trends such as urbanization common to most of the globe [4]. The higher flexibility enabled by digitalization requires that a certain level of disaggregation be captured by assessment models and tools, which cannot be framed by the traditional macroscopic transport models, i.e., models that consider aggregate people/vehicle flows This prompted a surge of activity-based and agent-based models (ABMs) resulting in an increase of data reliance and complexity, which is hindering their uptake and slowing down research [6]
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