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About

About

Research Assistant / I.T Project Manager.


Has experience in Machine Learning, Data-Mining, and Knowledge Discovery especially in areas such as Anomaly Detection, Time Series, and Mobility Patterns, Spatio-temporal data. 


Computer Science Ph.D. Student at the University of Porto - MAP-i. 

Holds a Computer Information Systems Bachelor degree and an MBA in Project Management. 


Project Management Professional (PMP), Agile enthusiast, Professional Scrum Master (PSM) e Scrum Fundamentals Certified, ITIL V3, COBIT Certified. 


More than 10 years of experience developing and managing applications for the Internet, desktop, and mobile. Extended experience with UML and BPMN modeling, as well as with the SCRUM process framework. 


Project Manager with over 10 years of experience coordinating software development teams running agile and traditional methodologies. 


Senior Business Analyst with over 8 years of experience collecting and structuring requirements from various sources, including end-user interviews, corporate stakeholders, documentation, and legacy system analysis. 


Specialties: Data Mining, Knowledge Discovery, Machine Learning, Design, and Systems Development, Deployment and Integration Solutions IT Consulting, and Project Management. 

Interest
Topics
Details

Details

  • Name

    Thiago Andrade Silva
  • Role

    Research Assistant
  • Since

    26th October 2016
  • Nationality

    Brasil
  • Contacts

    +351222094398
    thiago.a.silva@inesctec.pt
001
Publications

2023

Study on Correlation Between Vehicle Emissions and Air Quality in Porto

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

Publication
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

Authors
Andrade, T; Gama, J;

Publication
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

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

Publication
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

Authors
Andrade, T; Gama, J;

Publication
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

Authors
Andrade, T; Gama, J;

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

Abstract