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Sobre

Sobre

Assistente de Investigação.


Possui experiência em aprendizado de máquina, mineração de dados e extração de conhecimento de dados com especialidade em áreas como detecção de anomalias, séries temporais e padrões de mobilidade, dados espácio-temporais. 


Estudante do programa doutoral (Ph.D.) em Engenharia Informática na Universidade do Porto - MAPi

Bacharel em Sistemas de Informação e MBA em Gestão de Projetos. 


Possui certificações Project Management Professional (PMP), ITIL V3, COBIT, PSM e Scrum Fundamentals. 


Mais de 10 anos de experiência no desenvolvimento e manutenção de aplicações para Internet, Desktop e dispositivos móveis. Experiência adicional com modelagem UML e BPMN bem como framework Scrum. 


Gestor de Projetos com mais de 10 anos de experiência a coordenar equipas de desenvolvimento de software por meio da utilização de metodologias ágeis e tradicionais. 


Analista de Sistemas Sênior com mais de 8 anos de experiência na coleta e estruturação de requisitos de várias fontes, incluindo entrevistas com utilizadores, gestores executivos e na documentação e análise de sistemas legados. 


Especializações: Data Mining e Machine Learning, Projeto e Construção de Sistemas, Implantação e Integração de Soluções de Tecnololgia, Consultoria e Gestão de Projetos.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Thiago Andrade Silva
  • Cargo

    Assistente de Investigação
  • Desde

    26 outubro 2016
001
Publicações

2023

Study on Correlation Between Vehicle Emissions and Air Quality in Porto

Autores
Shaji, N; Andrade, T; Ribeiro, RP; Gama, J;

Publicação
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I

Abstract
Road transportation emissions have increased in the last few decades and have been the primary source of pollutants in urban areas with ever-growing populations. In this context, it is important to have effective measures to monitor road emissions in regions. Creating an emission inventory over a region that can map the road emission based on the vehicle trips can be helpful for this. In this work, we show that it is possible to use raw GPS data to measure levels of pollution in a region. By transforming the data using feature engineering and calculating the vehicle-specific power (VSP), we show the areas with higher emissions levels made by a fleet of taxis in Porto, Portugal. The Uber H3 grid system is used to decompose the city into hexagonal grids to sample nearby data points into a region. We validate our experiments on real-world sensor datasets deployed in several city regions, showing the correlation with VSP and true values for several pollutants attesting to the method's usefulness.

2023

Estimating Instantaneous Vehicle Emissions

Autores
Andrade, T; Gama, J;

Publicação
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023, Tallinn, Estonia, March 27-31, 2023

Abstract

2023

Pollution Emission Patterns of Transportation in Porto, Portugal Through Network Analysis

Autores
Andrade, T; Shaji, N; Ribeiro, RP; Gama, J;

Publicação
Progress in Artificial Intelligence - 22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Faial Island, Azores, September 5-8, 2023, Proceedings, Part I

Abstract
Over the past few decades, road transportation emissions have increased. Vehicles are among the most significant sources of pollutants in urban areas. As such, several studies and public policies emerged to address the issue. Estimating greenhouse emissions and air quality over space and time is crucial for human health and mitigating climate change. In this study, we demonstrate that it is feasible to utilize raw GPS data to measure regional pollution levels. By applying feature engineering techniques and using a microscopic emissions model to calculate vehicle-specific power (VSP) and various specific pollutants, we identify areas with higher emission levels attributable to a fleet of taxis in Porto, Portugal. Additionally, we conduct network analysis to uncover correlations between emission levels and the structural characteristics of the transportation network. These findings can potentially identify emission clusters based on the network’s connectivity and contribute to developing an emission inventory for an urban city like Porto.

2022

How are you Riding? Transportation Mode Identification from Raw GPS Data

Autores
Andrade, T; Gama, J;

Publicação
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022

Abstract
Analyzing the way individuals move is fundamental to understand the dynamics of humanity. Transportation mode plays a significant role in human behavior as it changes how individuals travel, how far, and how often they can move. The identification of transportation modes can be used in many applications and it is a key component of the internet of things (IoT) and the Smart Cities concept as it helps to organize traffic control and transport management. In this paper, we propose the use of ensemble methods to infer the transportation modes using raw GPS data. From latitude, longitude, and timestamp we perform feature engineering in order to obtain more discriminative fields for the classification. We test our features in several machine learning algorithms and among those with the best results we perform feature selection using the Boruta method in order to boost our accuracy results and decrease the amount of data, processing time, and noise in the model. We assess the validity of our approach on a real-world dataset with several different transportation modes and the results show the efficacy of our approach.

2020

Identifying Points of Interest and Similar Individuals from Raw GPS Data

Autores
Andrade, T; Gama, J;

Publicação
Mobility Internet of Things 2018 - EAI/Springer Innovations in Communication and Computing

Abstract