Publications

Publications by CEGI

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, LNICST

Abstract
In public transport, smart card-based ticketing system allows to redesign the UPT network, by providing customized transport services, or incentivize travelers to change specific patterns. However, in open systems, to develop personalized connections the journey destination must be known before the end of the travel. Thus, to obtain that knowledge, in this study three models (Top-K, NB, and J48) were applied using different groups of travelers of an urban public transport network located in a medium-sized European metropolitan area (Porto, Portugal). Typical travelers were selected from the segmentation of transportation card signatures, and groups were defined based on the traveler age or economic conditions. The results show that is possible to predict the journey’s destination based on the past with an accuracy rate that varies, on average, from 20% in the worst scenarios to 65% in the best. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

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2019

Driverless Cars-Another Piece of the Puzzle

Authors
Dias, TG;

Publication
IEEE TECHNOLOGY AND SOCIETY MAGAZINE

Abstract

2019

Real-Time Monitoring of a Mobile Ticketing Solution

Authors
Ferreira, MC; Universidade do Porto – Faculdade de Engenharia, Porto, Portugal,; Dias, TG; Cunha, JFe;

Publication
Journal of Traffic and Logistics Engineering

Abstract

2019

Codesign of a Mobile Ticketing Service Solution Based on BLE

Authors
Ferreira, MC; Universidade do Porto – Faculdade de Engenharia, Porto, Portugal,; Dias, TG; Cunha, JFe;

Publication
Journal of Traffic and Logistics Engineering

Abstract

2019

Handbook of Service Science, Volume II

Authors
Maglio, PP; Kieliszewski, CA; Spohrer, JC; Lyons, K; Patrício, L; Sawatani, Y;

Publication
Service Science: Research and Innovations in the Service Economy

Abstract

2019

A Data Mining Approach for Predicting Academic Success – A Case Study

Authors
Martins, MPG; Miguéis, VL; Fonseca, DSB; Alves, A;

Publication
Advances in Intelligent Systems and Computing

Abstract
The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure. © 2019, Springer Nature Switzerland AG.

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