The detailed study of individual human mobility requires large-scale high-resolution datasets, but collecting such datasets in a way that is both statistically powerful and privacy preserving is a challenging and expensive task. In response, researchers have built tools to generate complex synthetic populations of agents that can be used to simulate synthetic individual mobility data, potentially obviating the difficulties of data collection. While these simulation-based approaches offer a promising avenue for expanding individual mobility research, it is difficult to asses whether such tools are effective at generating realistic mobility traces. In this work, we develop a framework for comparing observed and simulated mobility data using a higher-order network framework that focuses on analyzing patterns of movement in the paths individuals take through the underlying infrastructure network. We apply our framework to a case study comparing the NetMob 2025 Data Challenge Dataset, which includes individual mobility data for thousands of residents of the Île-de-France region, with a sophisticated open-source synthetic population and mobility simulation model of the same region. We show that while simulated mobility data is indeed promising as a surrogate for observed mobility, there are some key limitations to the simulation paradigm from a path-based perspective, which we discuss along with potential future remediations and open challenges for higher-order mobility network analysis.

翻译：个体人类移动性的细致研究需要大规模高分辨率数据集，但以既具有统计效力又保护隐私的方式收集此类数据集是一项具有挑战性且成本高昂的任务。为此，研究人员开发了生成复杂合成代理群体的工具，可用于模拟合成个体移动数据，从而可能规避数据收集的困难。尽管这些基于模拟的方法为拓展个体移动性研究提供了有前景的途径，但评估此类工具能否有效产生逼真的移动轨迹仍十分困难。本文提出一个框架，利用高阶网络方法对比观测与模拟移动数据，重点分析个体在底层基础设施网络路径中的移动模式。我们将该框架应用于案例研究：对比NetMob 2025数据挑战赛数据集（包含法兰西岛地区数千名居民的个体移动数据）与同一地区的高精度开源合成群体及移动性模拟模型。结果表明，尽管模拟移动数据作为观测数据的替代方案确实具有潜力，但从路径视角来看，该模拟范式仍存在若干关键局限性。我们将结合未来可能改进方案及高阶移动性网络分析面临的开放挑战对此展开讨论。