Module manager: Dr Robin Lovelace
Email: r.lovelace@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
This module is not approved as an Elective
The surge in transport datasets creates a demand for skills many practitioners lack. This module bridges that gap by teaching AI-driven code development and open-source tools. You will master origin-destination modelling, routing, and visualisation on real-world data, empowering you to publish reproducible results for maximum societal benefit.
1. Understand the structure of transport datasets, from origin-destination to street segment levels. 2. Understand how to obtain, clean, and store transport-related datasets. 3. 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.
On successful completion of the module students will be able to: SSLO1. Applying data science to solve transport problems SSLO2. Proficiency in processing, visualization, and analysis of diverse transport datasets SSLO3. Original research skills transport planning/policy problems SSLO4. Critical evaluation of different transport data sources and methods for transport planning.
1. Software for practical data science 2. The structure of transport data at the level of zones, origin-destination pairs, routes, and infrastructure 3. AI for transport data science 4. Origin-destination data 5. Routing engines and network generation 6. Joins, models and publishing your work.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 6 | 1 | 6 |
| seminars | 2 | 3 | 6 |
| Practicals | 6 | 2 | 12 |
| Private study hours | 126 | ||
| Total Contact hours | 24 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Progress will be monitored via formative feedback from staff and peers during Practical 3, when each student is asked to discuss their plans for the Report and progress towards visualising a transport dataset related to a transport research question or real-world problem. This will take place in groups of students who will then feed back to the class. There will be one group per member of staff, enabling feedback, an opportunity for students to ask questions, and space for suggested changes of plan, e.g. because the proposal is too ambitious. In addition to summative feedback given on the Research Proposal, formative verbal feedback will be given to confirm students are on track and to identify students struggling to find a coursework topic or direction.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Lab Notebook | Coursework | 30 |
| Report | Coursework | 70 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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