2019/20 Taught Postgraduate Module Catalogue

TRAN5032M Transport Data Collection and Analysis

15 creditsClass Size: 100

Module manager: Dr Eva Heinen
Email: E.Heinen@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2019/20

Module replaces

TRAN5031M

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.

Objectives

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

Syllabus

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

Statistics:
- 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 type	Number	Length hours	Student hours
Revision Class	1	2.00	2.00
Fieldwork	1	6.00	6.00
Lecture	6	1.00	6.00
Lecture	6	2.00	12.00
Seminar	1	2.00	2.00
Seminar	6	1.00	6.00
Tutorial	1	2.00	2.00
Tutorial	6	1.00	6.00
Private study hours			108.00
Total Contact hours			42.00
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

Coursework

Assessment type	Notes	% of formal assessment
Report	2,000 words	50.00
Total percentage (Assessment Coursework)		50.00

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

Exams

Exam type	Exam 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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