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Defesa de Tese de Doutorado - Nº 455: Mining human mobility data and social media for smart ride sharing

O aluno Vinicius Cezar Monteiro de Lira defenderá seu trabalho no dia 22 de março, às 9h, no Auditório do CIn Início: 22/03/2019 às 09:00 Término: 22/03/2019 às 11:00 Local: Auditório do CIn

Pós-Graduação em Ciência da Computação – UFPE
Defesa de Tese  de Doutorado  Nº 455

Aluno: Vinicius Cezar Monteiro de Lira
Orientadora:  Profa. Valeria Cesario Times

Co-Orientador: Prof. Rossano Venturini (Università di Pisa)
Título: Mining human mobility data and social media for smart ride sharing
Data: 22/03/2019
Hora/Local: 9h – Centro de Informática - Auditório
Banca Examinadora:
Profa. Ana Carolina Brandão Salgado (UFPE / Centro de Informática)

Profa. Flávia de Almeida Barros (UFPE / Centro de Informática)

Prof. Luciano de Andrade Barbosa (UFPE / Centro de Informática)

Prof. Clodoveu Augusto Davis Junior – (UFMG/Deptº de Ciência da Computação) 
Prof. Cláudio de Souza Baptista (UFCG /Deptº de Sistemas e Computação)


RESUMO:

People living in highly-populated cities increasingly suffer an impoverishment of their quality of life due to pollution and traffic congestion problems caused by the huge number of circulating vehicles. Indeed, the reduction of circulating vehicles is one of the most difficult challenges in large metropolitan areas. This thesis proposes a research contribution with the final objective of reducing travelling vehicles. This is done towards two different directions: on the one hand, we aim to improve the efficacy of ride sharing systems, creating a larger number of ride possibilities based on the passengers destination activities. On the other hand, we want to propose a social media analysis method, based on machine learning, to identify groups of users who are going to participate in an event, with the objective of potentially enabling them to share a ride. 
Concerning the first research direction we investigate a novel approach to boost ride sharing opportunities based, not only on fixed destinations, but also on alternative destinations while preserving the intended activity of the user. We observe, in fact, that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed at many different locations (e.g. all the shopping malls in a given area). Our assumption is that, when there is the possibility of sharing a ride, people may accept visiting an alternative destination to fulfill their needs. By analyzing two large mobility datasets, extracted from a popular social network, we show that increasing individuals’ flexibility in changing destination could have a large impact on urban mobility. In fact, we found that with our approach there is an increase up to 54.69% in ride-sharing opportunities compared to a traditional fixed destination-oriented approach. We thus propose Activity-Based Ride Matching (ABRM), an algorithm aimed at matching ride requests with ride offers to alternative destinations where the intended activity can still be performed.
For the second research contribution, we focus on the analysis of social media for inferring the transportation demands for large events. We notice how popular events are well reflected in social media, where people share their feelings and discuss their experiences. In this context, we investigate the novel problem of exploiting the content of non-geotagged posts to infer users’ attendance of large events. We identified three temporal periods: before, during and after an event. We detail the features used to train the event attendance classifiers on the three temporal periods and report on experiments conducted on two large music festivals in the UK, namely VFestival and Creamfields events. Our classifiers attained very high accuracy, with the highest result observed for Creamfields festival (
91% accuracy to classify users that will participate in the event). We study the most informative features for the tasks addressed and the generalization of the learned models across different events. Furthermore, we proposed an example of application of our methodology in event-related transportation. This proposed application aimed to evaluate the geographic areas with a higher potential demand for transportation services to an event.


Palavras-chaveRide-sharing, Matching Algorithms, Activity-Based, Social Media, Attendance Prediction.

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