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2019/20 Taught Postgraduate Module Catalogue

TRAN5032M Transport Data Collection and Analysis

15 creditsClass Size: 100

Module manager: Dr Eva Heinen

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2019/20

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 differnt 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 tests and associated interpretation of results.

To develop students' ability to employ relevant software packages for data analysis and presentation.

Learning outcomes
Evaluate the link between research needs and data requirements and collection
Discuss the principles underlying statistical analysis and apply these on data
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


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
- Speed, delay and congestion
- Air quality data sources
- Rail and public transport data

- 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 face-to-face 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
Report2,000 words50.00
Total percentage (Assessment Coursework)50.00

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

Exam typeExam duration% of formal assessment
Unseen exam 3 hr 50.00
Total percentage (Assessment Exams)50.00

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: 11/09/2019


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