从聚合出行数据中提取城市级移动系统特征，揭示网络结构对人流的塑造作用。
Network-Level Measures of Mobility from Aggregated Origin-Destination Data
- 基于聚合出行数据构建系统级移动度量，关注网络结构而非个体行为
- 在真实数据中仍能识别出可解释的城市尺度流动模式和变化趋势
- 使用合成数据验证方法有效性，确保度量结果反映真实网络约束
我们提出一个框架，用于从空间和时间上聚合的起讫点（OD）数据中定义并解释集体移动度量。这些度量不刻画个体行为，而是描述移动系统本身的特性：网络结构、空间布局和路径约束如何塑造和引导人群流动。在此视角下，聚合流动揭示了底层交通与空间网络所编码的连通性、功能组织及大规模日常活动模式。为支持解释并提供可控参考，我们首先采用独立的、基于网络的合成数据生成器，其中轨迹源于预设系统结构而非观测数据。该受控环境为理解度量如何反映网络组织与流约束提供了明确基准。随后，我们将该方法应用于完全匿名化的2024年NetMob数据挑战赛数据，考察其在空间与时间聚合限制下的表现。尽管此类数据约束降低了动态分辨率，所得度量仍展现出可解释的大尺度结构与城市层面的时间变化。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。