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Unit of study_

CIVL9704: Transport Informatics

This unit of study offers students an introduction to civil engineering data analysis using examples of real-world transport operations applications. Students will develop skills to convert data into information for decision making including data ingestion, data structures, summarisation, visualisation, error analysis, and basic modelling. The data science skills will be taught using Python notebooks. In parallel with data science skills, this unit of study will introduce public transport system operations and planning. Lecture and reading content will provide a foundation of history, terminology and methods to assess the performance of public transport systems and make data-driven planning decisions. The datasets will be drawn from urban public transport applications, and explore real-world challenges in transport informatics.


Academic unit Civil Engineering
Unit code CIVL9704
Unit name Transport Informatics
Session, year
Semester 1, 2022
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

ENGG2851 OR CIVL3704
Assumed knowledge

Understanding of statistical inference. Familiarity with the urban transport network and basic concepts in transport studies

Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Emily Moylan,
Lecturer(s) Emily Moylan ,
Type Description Weight Due Length
Assignment Comprehension quizzes
Online quizzes
5% Multiple weeks n/a
Outcomes assessed: LO2 LO3 LO4 LO7 LO8 LO10
Assignment Reading quizzes
Online quizzes
5% Multiple weeks n/a
Outcomes assessed: LO2 LO3 LO4 LO5 LO7 LO10
Assignment Problem sets
Problem sets on Ed due Week 3,5
20% Please select a valid week from the list below n/a
Outcomes assessed: LO1 LO2 LO6 LO8 LO10 LO11
Assignment Project: Proposal
Short proposal for the data collection project
5% Week 07 n/a
Outcomes assessed: LO6 LO9 LO11
Assignment Project: visualising and presenting information
Submission of visualisation plus peer feedback
15% Week 08 n/a
Outcomes assessed: LO1 LO2 LO5 LO6 LO8 LO10
Assignment Project: Spatial Supplement
Creation of maps and analysis
15% Week 10 n/a
Outcomes assessed: LO1 LO2 LO6 LO8 LO9 LO10 LO11
Assignment Project: collecting and analysing data
Report on data collection, analysis and interpretation
35% Week 13 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO9 LO10 LO11
  • Reading quizzes: Short online quizzes accompany the provided readings. The mark is made of 10 equally weighted quizzes.
  • Comprehension quizzes: Short online quizzes accompany the e-lectures and guest lectures. The mark is made of 10 equally weighted quizzes.
  • Problem sets: Two python problem sets. Each problem set is worth 10%.
  • Project: visualising and presenting information:  This project will assess student’s ability to decompose a complex, open-ended problem, select and analyse relevant data and present the results visually and orally. Marks will be awarded on the content and clarity of the visualisations, the strength of the accompanying writing, and participation in a peer feedback exercise.
  • Project: proposal: Short proposal for using data to address an open-ended research problem. Marks will be awarded on evidence that the student has developed a relevant research question with an appropriate data source and methodology. 
  • Project: spatial visualisation: The spatial visualisation will allow the student to revisit their visualisation skills while employing spatial data.  The maps should reflect the topic presented in the Project Proposal. Marks will be awarded for the relevance, accuracy, scope and presentation of the analysis.
  • Project: collecting and analysing data: This project will assess student’s ability to use data to analyse a real-world public transport operations issue. The mark will include components for data collection, data processing/information retrieval, modelling and interpretation. It should build on material prepared for the Visualisation, Project Proposal and Spatial Visualisation assessments. 


Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. identify evidence of theoretical issues in the data and evaluate their significance
  • LO2. present data-focused analysis in visual and oral contexts
  • LO3. demonstrate understanding of the broader context for public transit including regulatory, equity, economic and environmental considerations
  • LO4. demonstrate knowledge of ethical issues and professional standards around the gathering and use of transport data
  • LO5. demonstrate an interdisciplinary evaluation of the public transit system including social, environmental and economic perspectives
  • LO6. decompose complex problems into tasks in a systematic way
  • LO7. employ public transport terminology fluently
  • LO8. perform calculations related to public transport planning and operations
  • LO9. develop solutions to open-ended public transit questions and support the solutions with evidence
  • LO10. apply data science tools to analyse public transport systems
  • LO11. select and apply appropriate modelling techniques. Apply theoretical understanding of statistical methods to practical problems around data collection, statistical inference and interpretation.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
In response to student feedback, the python start-up will be supported with formative assessment.


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