MQAT

Exercises in R

Authors

Rosa Félix

Gabriel Valença

Filipe Moura

Published

November 25, 2025

1 Introduction

Materials, exercises, data and tutorials for the Quantitative Methods of Analysis in Transportation course of the MSc in Transportation Systems at Instituto Superior Técnico - University of Lisbon.
This is not the official website for the course.

Please refer to Fénix: Quantitative Methods of Analysis in Transportation

2 Introduction

This website aims to provide tools to deal with transportation datasets using R programming, an open-source and widely used tool for data analytics in urban mobility.

It includes course sporting materials, exercises, and examples to explore with R, with a focus on modelling and spatial data analysis using GIS techniques.

Why R and GIS

Most academic programs focus on teaching modelling and deep analysis of data. However, there is a need to learn how to explore and prepare a dataset for modelling. The use of programming and GIS techniques have enormous advantages, including their flexibility; reproducibility; and transparency and understanding the step-by-step process.

The use of GIS techniques in transportation is of enormous relevance when doing accessibility analysis or reeling with georreferenced transportation data, such as bike sharing route trips’ datasets, origin-destination flows datasets, home/work locations, GTFS public transit data, and so on. There is a need to learn how to locate these open datasets, how to explore them and how to integrate them into transportation and urban analysis. Additionally, the use of open source software and datasets allows researchers to perform methods that are reproducible and transparent.

TLDR

  • Open-source tools widely used in data analytics and spatial analysis

  • Flexibility and reproducibility in data manipulation and visualization

  • Critical for urban mobility and transportation research, with spatial relevance

  • Large transportation datasets are becoming increasingly common