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TDS (Transport Data Science)

This is a GitHub Repository (repo for short) that supports teaching of the Transport Data Science module at the University of Leeds. The module can be taken by students on the Data Science and Analytics and the Transport Planning and the Environment MSc courses.

The module timetable can be downloaded as an ical (.ics) file and added to your calendar here. It is also shown in the table below.

Summary Description Date Location Duration (Hours)
TDS Lecture 1: structure The structure of transport data and data cleaning 2023-01-30 Civil Engineering LT B (3.25) 60
TDS deadline 1 Computer set-up 2023-02-03 Online - Teams 1
TDS Lecture 2: od Working with origin-destination data 2023-02-06 Civil Engineering LT B (3.25) 60
TDS Practical 1: structure The structure of transport data 2023-02-09 West Teaching Lab Cluster (G.29) 150
TDS Lecture 3: routing From origin-destination data to routes 2023-02-13 Civil Engineering LT B (3.25) 60
TDS Practical 2: routing Routing 2023-02-16 West Teaching Lab Cluster (G.29) 150
TDS Practical 3: od Origin-destination data 2023-02-23 West Teaching Lab Cluster (G.29) 150
TDS deadline 2 Draft portfolio 2023-02-24 Online - Teams 1
TDS Practical 4: getting Getting transport data 2023-03-02 West Teaching Lab Cluster (G.29) 150
TDS Lecture 4: viz Visualising transport data 2023-03-20 Civil Engineering LT B (3.25) 60
TDS Lecture 5: project Project work 2023-03-27 Civil Engineering LT B (3.25) 60
TDS seminar 1 Seminar 2023-04-19 Institute for Transport Studies 1.11 180
TDS Practical 5: project Project work 2023-05-04 West Teaching Lab Cluster (G.29) 150
TDS deadline 3 Deadline: coursework, 2pm 2023-05-19 Online - Teams 1



For this module you need to have up-to-date versions of R and RStudio installed on a computer you have access to:

  • R from
  • RStudio from
  • R packages, which can be installed by opening RStudio and typing install.packages("stats19") in the R console, for example.

You should have the latest stable release of R (4.0.0 or above) running R on a decent computer, with at least 4 GB RAM and ideally 8 GB or more RAM. See Section 1.5 of the online guide Reproducible Road Safety Research with R for instructions on how to install key packages we will use in the module.1

It is also recommended that you have installed and have experience with GitHub Desktop (or command line git on Linux and Mac), Docker, Python, QGIS and transport modelling tools such as SUMO and A/B Street. These software packages will help with the course but are not essential.

Data science experience

Attending the Introduction to R one-off 3 hour workshop (semester 1 Computer Skills workshop) and experience of using R (e.g. having used it for work, in previous degrees or having completed an online course) is essential. Students can demonstrate this by showing evidence that they have worked with R before, have completed an online course such as the first 4 sessions in the RStudio Primers series or DataCamp’s Free Introduction to R course: This is an advanced and research-led module. Evidence of substantial programming and data science experience in previous professional or academic work, in languages such as R or Python, also constitutes sufficient pre-requisite knowledge for the course.

Course reading

See the handbook.

Assessment (for those doing this as credit-bearing)

  • You will build-up a portfolio of work
  • 100% coursework assessed, you will submit by Friday 19th May:
    • a pdf document up to 10 pages long with figures, tables, references explaining how you used data science to research a transport problem
    • reproducible code contained in an RMarkdown (.Rmd) document that produced the report
  • Written in RMarkdown - will be graded for reproducibility
  • Code chunks and figures are encouraged
  • You will submit a non-assessed 2 page pdf + Rmd report by Friday 26th March

Issues and contributing

Any feedback or contributions to this repo are welcome. If you have a question please open an issue here (you’ll need a GitHub account):



  1. For further guidance on setting-up your computer to run R and RStudio for spatial data, see these links, we recommend Chapter 2 of Geocomputation with R (the Prerequisites section contains links for installing spatial software on Mac, Linux and Windows): and Chapter 2 of the online book Efficient R Programming, particularly sections 2.3 and 2.5, for details on R installation and set-up and the project management section.