Knowledge-Assisted Visualization of Multi-Level Origin-Destination Flows Using Ontologies

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Thiago Sobral (Autor) Outra; Teresa Galvão Dias (Autor) FEUP; José Luís Moura Borges (Autor) FEUP

Revista

Título: IEEE Transactions on Intelligent Transportation Systems

Vol. 22

Páginas: 2168-2177

ISSN: 1524-9050

Editora: IEEE

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00T-GVW

DOI: 10.1109/tits.2021.3056228

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10