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001
Publications

2020

An Ontology-based approach to Knowledge-assisted Integration and Visualization of Urban Mobility Data

Authors
Sobral, T; Galvao, T; Borges, J;

Publication
Expert Systems with Applications

Abstract
This paper proposes an ontology-based framework to support integration and visualization of data from Intelligent Transportation Systems. These activities may be technically demanding for transportation stakeholders, due to technical and human factors, and may hinder the use of visualization tools in practice. The existing ontologies do not provide the necessary semantics for integration of spatio-temporal data from such systems. Moreover, a formal representation of the components of visualization techniques and expert knowledge can leverage the development of visualization tools that facilitate data analysis. The proposed Visualization-oriented Urban Mobility Ontology (VUMO) provides a semantic foundation to knowledge-assisted visualization tools (KVTs). VUMO contains three facets that interrelate the characteristics of spatio-temporal mobility data, visualization techniques and expert knowledge. A built-in rule set leverages semantic technologies standards to infer which visualization techniques are compatible with analytical tasks, and to discover implicit relationships within integrated data. The annotation of expert knowledge encodes qualitative and quantitative feedback from domain experts that can be exploited by recommendation methods to automate part of the visualization workflow. Data from the city of Porto, Portugal were used to demonstrate practical applications of the ontology for each facet. As a foundational domain ontology, VUMO can be extended to meet the distinctiveness of a KVT. © 2020

2020

Process discovery on geolocation data

Authors
Ribeiro, J; Fontes, T; Soares, C; Borges, JL;

Publication
Transportation Research Procedia

Abstract

2020

A Deep Learning Approach for Predicting Bus Passenger Demand Based on Weather Conditions

Authors
Fontes, T; Correia, R; Ribeiro, J; Borges, JL;

Publication
Transport and Telecommunication Journal

Abstract
AbstractThis work apply a deep learning artificial neural network model – the Multilayer Perceptron – as a regression model to estimate the demand of bus passengers. Transit bus ridership and weather conditions were collected over a year from a medium-size European metropolitan area and linked under the assumption: individuals choose the travel mode based on the weather conditions that are observed during (a) the departure hour, (b) the hour before or (c) two hours prior to the travel start. The transit ridership data were also labelled according to the hour of the day, day of the week, month, and whether there was a strike and/or holiday or not. The results show that the prediction error of the model decrease by ~9% when the weather conditions observed two hours before travel start is taken into account. The model sensitivity analyses reveals that the worst performance is obtained for a strike day of a weekday in spring (typically Wednesdays or Thursdays).

2019

Visualization of urban mobility data from intelligent transportation systems

Authors
Sobral, T; Galvao, T; Borges, J;

Publication
Sensors (Switzerland)

Abstract
Intelligent Transportation Systems are an important enabler for the smart cities paradigm. Currently, such systems generate massive amounts of granular data that can be analyzed to better understand people’s dynamics. To address the multivariate nature of spatiotemporal urban mobility data, researchers and practitioners have developed an extensive body of research and interactive visualization tools. Data visualization provides multiple perspectives on data and supports the analytical tasks of domain experts. This article surveys related studies to analyze which topics of urban mobility were addressed and their related phenomena, and to identify the adopted visualization techniques and sensors data types. We highlight research opportunities based on our findings. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

2019

Prediction of Journey Destination for Travelers of Urban Public Transport: A Comparison Model Study

Authors
Costa, V; Fontes, T; Borges, JL; Dias, TG;

Publication
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Intelligent Transport Systems, From Research and Development to the Market Uptake

Abstract

Supervised
thesis

2019

Uma metodologia para avaliação de desempenho de um processo de conceção de um produto

Author
Francisca Inês Finz de Carvalho Braga César

Institution
UP-FEUP

2019

A data driven approach for the performance evaluation of urban public transport systems

Author
Vera Lúcia Freitas da Costa

Institution
UP-FEUP

2019

Semantic Integration of Urban Mobility Data through Ontologies for Supporting Data Visualization

Author
Thiago Sobral Marques da Silva

Institution
UP-FEUP

2019

Pull by Pushing: Aplicação de um Modelo Híbrido de Planeamento Industrial

Author
João de Sousa Soares de Sousa Guedes

Institution
UP-FEUP

2019

Implementação de Metodologias de Gestão da Qualidade Total na Indústria da Cortiça

Author
Catarina Lima Carneiro Marques dos Santos

Institution
UP-FEUP