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Publications

2021

Performance assessment of upper secondary schools in Italian regions using a circular pseudo-Malmquist index

Authors
Camanho, AS; Varriale, L; Barbosa, F; Sobral, T;

Publication
European Journal of Operational Research

Abstract
This paper investigates the relationship between students’ performance and the type of school attended during upper secondary education. The performance of three different types of schools (Liceo, Technical and Professional schools) in four Italian macroregions (North West, North East, Centre, South & Islands) is investigated. A benchmarking analysis of the variability in students’ performance among regions (within macroregions) for cohorts of students attending Liceo is also conducted. The data was collected at the student level from the Italian Institute for the Evaluation of Education System (INVALSI), for the academic year 2017/18. Families with higher socio-economic status may self-select into Liceo, so a direct comparison with vocational schools could lead to biased conclusions regarding the impact of school type on student performance. To overcome this limitation, we used a Propensity Score Matching approach prior to the estimation of efficiency. A pseudo-Malmquist index, based on a metafrontier and satisfying the circular property, is developed. It enables comparing the location of the best-practice frontier for each type of school and the spread in the educational efficiency of the students attending each type of school. Thus, best performance of a given school type corresponds to the combined effect of these two aspects. This study is an interesting starting point to challenge the stereotypes that persist in Italy, especially concerning general and vocational studies and geographic differences in educational achievements. © 2020 Elsevier B.V.

2021

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

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

Publication
IEEE Transactions on Intelligent Transportation Systems

Abstract

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

Automatic Generation of Spider Maps for Providing Public Transports Information

Authors
Santos, S; Dias, TG; Sobral, T;

Publication
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

Abstract
With the continuous growth and complexity of public transport systems, it is essential that the users have access to transport maps that help them easily understand the underlying network, thus facilitating the user experience and public transports ridership. Spider Maps combine elements from geographical and schematic maps, to allow answering questions like “From where I am, where can I go?”. Although these maps could be very useful for travellers, they still are mostly manually generated and not widely used. Moreover, these maps have several design constraints, which turns the automation of the generation process into a complex problem. Although optimisation techniques can be applied to support the generation process, current solutions are time expensive and require heavy computational power. This paper presents a solution to automatically generate spider maps. It proposes an algorithm that adapts current methods and generates viable spider map solutions in a short execution time. Results show successful spider maps solutions for areas in Porto city. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

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.