We introduce a framework for defining and interpreting collective mobility measures from spatially and temporally aggregated origin--destination (OD) data. Rather than characterizing individual behavior, these measures describe properties of the mobility system itself: how network organization, spatial structure, and routing constraints shape and channel population movement. In this view, aggregate mobility flows reveal aspects of connectivity, functional organization, and large-scale daily activity patterns encoded in the underlying transport and spatial network. To support interpretation and provide a controlled reference for the proposed time-elapsed calculations, we first employ an independent, network-driven synthetic data generator in which trajectories arise from prescribed system structure rather than observed data. This controlled setting provides a concrete reference for understanding how the proposed measures reflect network organization and flow constraints. We then apply the measures to fully anonymized data from the NetMob 2024 Data Challenge, examining their behavior under realistic limitations of spatial and temporal aggregation. While such data constraints restrict dynamical resolution, the resulting metrics still exhibit interpretable large-scale structure and temporal variation at the city scale.

翻译：本文提出一个从时空聚合的起点-终点数据中定义与解释集体流动性度量的框架。这些度量并非刻画个体行为，而是描述流动性系统本身的特性：网络组织、空间结构与路径约束如何塑造并引导人口流动。在此视角下，聚合流动量揭示了底层交通与空间网络中蕴含的连通性、功能组织及大规模日常活动模式等特征。为支撑理论解释并为所提出的时间演化计算提供受控参照，我们首先采用独立于观测数据的网络驱动合成数据生成器，其中轨迹源于预设的系统结构而非实际观测。这一受控环境为理解所提度量如何反映网络组织与流动约束提供了具体参照。随后我们将该度量框架应用于NetMob 2024数据挑战赛的完全匿名化数据，考察其在现实时空聚合限制下的表现。尽管此类数据约束会降低动态分辨率，所得度量仍能在城市尺度上呈现出可解释的大规模结构与时间变化特征。