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2018/19 Taught Postgraduate Module Catalogue

TRAN5032M Transport Data Collection and Analysis

15 creditsClass Size: 100

Module manager: Dr Eva Heinen

Taught: Semester 1 View Timetable

Year running 2018/19

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 generation, data analysis, and interpretation of evidence. It covers data collection techniques, statistical methods, as well as relevant software packages, relevant to each students programme of study.


To ensure that students understand the needs of data generation, 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 knowledge of transport data relevant to their programme of study (e.g. engineering, planning, and environment).

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

Learning outcomes
Understand the link between research needs and data generation
Be familiar with data collection techniques relevant to transport issues
Develop data acquisition skills
Develop data handling, statistical and analytical skills
Develop data presentation and reporting skills


Data collection methods (for all students):
- Characteristics of transport data, data sources and data management
- Trip matrix data
- Road accident data
- Observational data via driving simulators and field trials
- Automatic traffic counting and air quality data
- Technology aided data collection

Statistics (for all students)::
- Definition of variables and summary measures
- Measures of dispersion
- Normal distribution
- Distribution of sample mean
- Hypothesis testing
- Contingency tables
- Regression analysis
- Poisson distribution

For the Engineering stream:
- Road and furniture inventory
- Road condition
- Speed, delay and congestion

For the Planning Stream:
- Questionnaire design
- Crowd sourcing data collection and analysis
- Concept and process of big data

For the Environment and Sustainability Stream:
- Air quality framework, strategy, standards and data sources
- Meteorological process, monitoring technologies and data sources

Teaching methods

Delivery typeNumberLength hoursStudent hours
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 2 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: 12/12/2018 16:33:10


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