2024/25 Taught Postgraduate Module Catalogue

TRAN5340M Transport Data Science

15 creditsClass Size: 40

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

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

This module is not approved as an Elective

Module summary

The quantity, diversity and availability of datasets on transport behaviour, infrastructure and outcomes are increasing, creating huge demand for data science skills in transport planning and related sectors. This module takes a practical approach to learning about data science tools and their application to transport issues, with a focus on geocomputation, visualisation and modelling. The course empowers students to deliver data science projects, answer transport questions, and use cutting-edge data science techniques on real-world datasets.

Objectives

1. Understand the structure of transport datasets, from origin-destination to street segment levels.
2. Understand how to obtain, clean, and store transport-related data.
3. Gain proficiency in command-line tools for handling large transport datasets.
4. Produce data visualizations, both static and interactive via web maps.
5. Learn where to find large transport datasets and assess data quality.
6. Learn how to join together the components of transport data science into a cohesive project portfolio.

Learning outcomes
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
Specifically, learning outcomes are:
1. Identify available datasets and access and clean them.
2. Combine datasets from multiple sources.
3. Visualize and communicate the results of transport data science, and know about setting up interactive web applications.
4. Articulate the importance of data science in a wider context.

Skills learning outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Understand the structure of transport datasets, from origin-destination to street segment levels.
2. Understand how to obtain, clean and store transport related data.
3. Gain proficiency in command-line tools for handling large transport datasets.
4. Produce data visualizations, static and via interactive web maps.
5. Learn where to find large transport datasets and assess data quality.
6. Learn how to join together the components of transport data science into a cohesive project portfolio.

Competence Standards
The ability to deliver high quality reproducible data science work.

Syllabus

Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module

Teaching methods

Delivery type	Number	Length hours	Student hours
seminars	2	3.00	6.00
Practicals	6	3.00	18.00
Private study hours			126.00
Total Contact hours			24.00
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

Progress will be monitored informally during supervised practical sessions.

Feedback given on a two page plan to confirm they are on track and to identify students struggling to find a coursework topic or direction.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Coursework	100.00
Total percentage (Assessment Coursework)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 10/04/2024

Disclaimer

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