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.

翻译：在科学文献和现实生活应用领域,基于代理的建模在运输领域引起了人们的注意,这是因为它有能力处理运输系统日益复杂的日益复杂的问题,包括未来干扰的移动技术和服务,例如自动化驾驶、服务流动和微流动。出现了不同的软件,专门用来模拟旅行需求分类,根据每个代理的特征作出个人选择。但是,利用这些模型的实际研究工作仍然稀少,而且能够使用这些模型的专业人员很少。这可以归因于大量需要的投入数据,或者缺乏商业解决办法和研究成果,无法详细说明导致实际模拟的过程。在本论文中,通过利用公开提供的综合数据进行空间分配的方法,在案例研究中加以实施。在这样做时,该文件为快速和有效地处理基于活动的需求生成所需的数据提供了系统的方法。最后,对所获得的数据集作了描述,它代表着爱沙尼亚塔林市的合成人口,其空间分配情况,因此可供其他研究人员加以利用,因为空间分配和结果数据来源都需要的工具都是开放的。