Origin-destination matrices help understand the movement of people within cities. This work is built upon the premise that stakeholders, e.g. decision makers, need to analyze mobility flows from spatio-temporal perspectives that are appropriate to their context of analysis. The data retrieved from sensors and Intelligent Transportation Systems are useful for this purpose due to their lower acquisition costs and fine granularity, although it is complex to use such data in an integrated way, as they might have heterogeneous representations of spatio-temporal attributes and granularities. Most of the related works on the analysis of OD flows consider matrices with a fixed spatio-temporal aggregation level, and do not explore the intrinsic issue of data heterogeneity. Herein we report our findings on building the semantic foundation of knowledge-assisted visualization tools for analyzing OD matrices from multiple stakeholder levels. We propose a set of ontology design patterns for modeling the semantics of OD data, and the relations between the spatio-temporal constructs that stakeholders ought to choose when visualizing urban mobility flows. Our approach aims to be reusable by researchers and practitioners. We describe a practical implementation using estimated flows from smart card data from Porto, Portugal.

中文翻译：

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使用本体的知识辅助的多层次起点流可视化

始发地目的地矩阵有助于了解城市中人们的流动情况。这项工作的前提是，利益相关者（例如决策者）需要从时空的角度分析适合其分析背景的流动性。从传感器和智能交通系统检索的数据由于其较低的购置成本和精细的粒度而可用于此目的，尽管以集成方式使用此类数据很复杂，因为它们可能具有时空属性和粒度的异构表示形式。有关OD流量分析的大多数相关工作都考虑了具有固定时空聚合水平的矩阵，而没有探讨数据异质性的内在问题。本文中，我们报告了有关建立知识辅助可视化工具的语义基础的发现，该工具可用于分析来自多个利益相关者级别的OD矩阵。我们提出了一套本体设计模式，用于对OD数据的语义进行建模，以及在可视化城市交通流动时利益相关者应选择的时空构造之间的关系。我们的方法旨在被研究人员和从业人员重用。我们使用来自葡萄牙波尔图的智能卡数据的估计流量来描述一种实际的实现方式。我们的方法旨在被研究人员和从业人员重用。我们使用来自葡萄牙波尔图的智能卡数据的估计流量来描述一种实际的实现方式。我们的方法旨在被研究人员和从业人员重用。我们使用来自葡萄牙波尔图的智能卡数据的估计流量来描述一种实际的实现方式。