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2023/24 Taught Postgraduate Module Catalogue

TRAN5032M Transport Data Collection and Analysis

15 creditsClass Size: 250

Module manager: Dr Chiara Calastri

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2023/24

Module replaces


This module is not approved as an Elective

Module summary

This module provides fundamentals of data collection and analysis in the context of transport. It addresses the loop covering research questions, data requirements, data collection/generation, data analysis, and interpretation.


To ensure that students understand the quality of different data and data collection techniques and can discuss their strengths and weaknesses.

To develop student's ability to design transport data collection activities.

To ensure that students understand the needs for data generation and approaches to collecting a range of transport data, and are able to justify techniques appropriate for given research requirements.

To ensure that students understand the principles underlying the statistical analysis of transport data and are equipped to select appropriate statistical techniques and associated interpretation of results.

To develop students' ability to interpret the result of statistical analyses and draw meaningful conclusions.

Learning outcomes
• Evaluate the link between research needs and data requirements and collection
• Discuss the principles underlying statistical analysis, apply these on data, and interpret their results
• Discuss and recommend data collection techniques relevant to transport issues
• Apply data acquisition skills
• Apply data handling, statistical and analytical skills
• Apply data presentation and reporting skills


Indicative data collection methods:
- Characteristics of (transport) data
- Data and Research design
- Primary and secondary data
- Automatic data collection / Technology aided data collection
- Qualitative data
- Questionnaire design
- Big data
- Air quality data sources
- Rail and public transport data

Indicative statistical techniques:
- Definition of variables and summary measures
- Measures of dispersion
- Normal distribution
- Distribution of sample mean
- Hypothesis testing
- Contingency tables
- Regression analysis
- Poisson distribution

Teaching methods

Delivery typeNumberLength hoursStudent hours
Revision Class12.002.00
Private study hours108.00
Total Contact hours42.00
Total hours (100hr per 10 credits)150.00

Private study

Sufficient effort is expected for further reading after each lecture, as well as for seeking relevant literature and collating evidence across a range of sources for completing the coursework. Support to private study will be offered by discussions, as well as by VLE forums.

Opportunities for Formative Feedback

Students learning progress will be monitored by the coursework process. Students will be fully supported during the process of completing their coursework via a variety of channels including physical discussions as well as virtual environment (e.g. VLE forums, emails etc). Feedback on coursework performance will be provided assisting the students to understand weakness in the submitted work and suggesting ways that work can be improved.

Methods of assessment

Assessment typeNotes% of formal assessment
Report800 Words50.00
Total percentage (Assessment Coursework)50.00

The change in the coursework has to do with its length. Previously students were given two data sources to analyse, now they will be given a single one. They will still be required to apply Statistical techniques and discuss data quality and implications of their work, hence meeting the same learning objectives.

Exam typeExam duration% of formal assessment
Online Time-Limited assessment2 hr 00 mins50.00
Total percentage (Assessment Exams)50.00

The exam will be the same as before, with the only exception of the fact that a word limit will be enforced on one of the questions. This is a typically open-ended question, and the word count is enforced to ensure that the students give focused answers rather than including a large amount of background material, so that their contribution can be better assessed.

Reading list

The reading list is available from the Library website

Last updated: 16/10/2023 15:49:47


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