Paper Info
Title
Knowledge-Assisted Visualization of Multi-Level Origin-Destination Flows Using Ontologies
Abstract
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.
Year
DOI
Venue
2021
10.1109/TITS.2021.3056228
IEEE Transactions on Intelligent Transportation Systems
Keywords
DocType
Volume
Ontologies,origin-destination matrices,data visualization,Semantic Web
Journal
22
Issue
ISSN
Citations 
4
1524-9050
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Thiago Sobral151.42
Teresa GalvãO2205.73
José Borges314412.93