Large-Scale Cities Synthesis 

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 research stream, methodologies to spatially assign a synthetic population by exploiting only publicly available aggregate data, the assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics, large scale calibration of both demand and supply models are proposed, providing a systematic approach for an efficient treatment of the data needed for agent based and activity-based model generation.
 

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