2020/21 Taught Postgraduate Module Catalogue

TRAN5340M Transport Data Science

15 creditsClass Size: 30

Module manager: Dr Robin Lovelace
Email: r.lovelace@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2020/21

Pre-requisite qualifications

Acceptance on to any of the Masters programmes at the Institute for Transport Studies or equivalent experience (if taken as an individual module).

There are no pre-requisite modules but the Introduction to R one-off 3 hour workshop (semester 1 Computer Skills workshop) is recommended (or equivalent experience using R, e.g. by completing the free online 4 hour tutorial at https://www.datacamp.com/courses/free-introduction-to-r )

This module is not approved as an Elective

Module summary

The quantity, diversity and availability of transport data is increasing rapidly, requiring skills in the management and interrogation of data and databases. We are in the midst of an accelerating data revolution and data science have never been more important in the work place. Transport planners, consultancies and researchers increasingly need to take data from a wide range of sources and perform non-standard analyses methods on them to inform the decision-making process. There is high demand for “skilled technical talent capable of handling and analysing very large datasets compiled from multiple sources” in the transport sector according to the Transport Systems Catapult. This module teaches the skills needed to help get a high paid job in this context. More importantly, you will be empowered to support sustainable transport policies using new techniques and datasets, ranging from openly available origin-destination datasets to huge datasets from global positioning systems (GPS). Not only does the module teach data skills, it also teaches the importance of understanding how advanced data analysis, modelling and visualisation can support the global transition away from fossil fuels.

Objectives

• Understand the structure and origin of large transport datasets
• Proficiency in command-line tools for handling large transport datasets
• Knowledge of obtaining, cleaning and storing transport data
• Produce data visualizations, static and interactive
• Apply advanced methods, including geographic analysis, modelling or machine learning techniques
• Consolidate transport data science skills in a cohesive project with real-world relevance

Learning outcomes
• Become confident and skilled working with large and diverse transport datasets
• Ability to identify appropriate datasets and use ‘the right tool for the job’ from the data scientist’s toolkit to answer transport research questions
• Understanding the landscape of transport data science applications and job market and be able to apply data science skills for applied transport planning, research and consultancy projects

Skills outcomes
Students will gain skills in:
- Importing a range of transport data file formats
- Setting-up data science projects to ensure reproducibility
- Data cleaning and manipulation
- Visualisation of large datasets

Syllabus

- Software for practical data science
- The structure of transport data e.g. flows, incidents, origin/destination, GIS
- Data cleaning and subsetting
- Accessing data from web sources
- Processing data using remote services and locally installed software
- Data visualisation
- Machine learning
- Professional and ethical issues of big data in transport data analysis

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	5	1.00	5.00
Practical	5	3.00	15.00
Seminar	5	1.00	5.00
Private study hours			125.00
Total Contact hours			25.00
Total hours (100hr per 10 credits)			150.00

Private study

Students are expected to spend their study time on software set-up and worked examples, plus background reading for lectures, preparatory work for workshops and assessed coursework.
Unsupervised teamwork practical sessions will be arranged to ensure a complete portfolio is submitted. Students are encouraged to submit the code that generated the portfolio.

Opportunities for Formative Feedback

Progress will be monitored informally during supervised practical sessions.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Portfolio	Project Portfolio 3000 word limit plus appendices	100.00
Total percentage (Assessment Coursework)		100.00

The project portfolio will be a 3000 word limit, (excluding appendices) report explaining the methods learned and their application to real problems. Students are encouraged to submit reproducible code alongside the report in the appendices. This will be used as the basis for formative assessment mid-way through the semester. This will highlight if any students are struggling. If a portfolio fails the assessment criteria the student will have an opportunity to resubmit a report outlining what they have learned in the areas in which they are failing.

Reading list

The reading list is available from the Library website

Last updated: 24/09/2020 15:38:22

Disclaimer

Browse Other Catalogues

• Undergraduate module catalogue
• Taught Postgraduate module catalogue
• Undergraduate programme catalogue
• Taught Postgraduate programme catalogue

Errors, omissions, failed links etc should be notified to the Catalogue Team.PROD