使用公开数据的基于活动建模的合成人口分配
Assignment of a Synthetic Population for Activity-based Modelling employing Publicly Available Data
摘要：
基于智能体的建模已经在科学文献和现实生活应用中获得了交通领域的关注，这要归功于其处理交通系统日益复杂的能力，包括未来破坏性的移动技术和服务，如自动驾驶，移动性即服务和微移动性。出现了不同的软件，专门用于模拟分类的旅行需求，根据每个代理人的个人资料制定个人选择。尽管如此，利用这些模型的实际研究工作是稀缺的，拥有使用它们的知识的专业人员也很少。这可能是由于需要大量的输入数据，或者缺乏商业解决方案和详细说明实际模拟过程的研究成果。在本文中，一种方法来空间分配一个合成人口利用公开的聚合数据，并实施了一个案例研究。在这样做的过程中，该文件提供了一个系统的方法，以快速和有效地处理基于活动的需求生成所需的数据。最后，所获得的数据集，代表了爱沙尼亚塔林市的合成人口，其空间分配，进行了描述，以便它可以被研究人员利用，因为空间分配所需的工具和由此产生的数据集都是作为开源的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
