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Unit outline_

CIVL3704: Transport Informatics

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

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.

Unit details and rules

Academic unit Civil Engineering
Credit points 6
Assumed knowledge

MATH1005 AND CIVL2700. 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

Coordinator Emily Moylan,
Lecturer(s) Emily Moylan,
Type Description Weight Due Length
Assignment Reading quizzes
Online quizzes
5% - n/a
Outcomes assessed: LO2 LO3 LO4 LO5 LO7 LO10
Assignment Comprehension quizzes
Online quizzes
5% - n/a
Outcomes assessed: LO2 LO3 LO4 LO7 LO8 LO10
Assignment Problem sets
Problem sets on Ed due Week 3, 5
20% - n/a
Outcomes assessed: LO1 LO2 LO6 LO8 LO10 LO11 LO12
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 LO6 LO8 LO9 LO10 LO11 LO12
Assignment Project: Spatial Supplement
Creation of maps and analysis
15% Week 10 n/a
Outcomes assessed: LO1 LO2 LO6 LO8 LO9 LO10 LO11 LO12
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 LO12

Assessment summary

  • 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 guide to grades.

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.

Academic integrity

The Current Student website  provides information on academic integrity 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 integrity breaches seriously.  

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

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to public transport and informatics Lecture (1 hr) LO3 LO7 LO10
Getting started with Jupyter Workshop (2 hr) LO10
Week 02 The importance of data Practical (1 hr) LO10 LO12
Reading and manipulating data with Python Workshop (2 hr) LO10
Week 03 Data for decision making Seminar (1 hr) LO3
Data Collection (1) Workshop (2 hr) LO1 LO4 LO9
Week 04 Transport Informatics Project Lecture (1 hr) LO3 LO6 LO9
Formulating a research question Workshop (2 hr) LO6 LO9
Week 05 Principles of Visualisation Seminar (1 hr) LO1 LO2 LO10
How to plot Workshop (2 hr) LO2 LO9
Week 06 Data collection (2) Workshop (1 hr) LO8 LO10
Data collection (2) Workshop (2 hr) LO1 LO8 LO10
Week 07 Spatial data and visualisations Lecture (1 hr) LO1 LO2
Making Maps Workshop (2 hr) LO2 LO10
Week 08 Making Maps (2) Python Workshop (1 hr) LO2 LO10
Making Maps (3) GIS Software Workshop (2 hr) LO2 LO10
Week 09 Applications of data and models to transport operations Seminar (1 hr) LO3 LO4 LO10
Models 1: All models are wrong but some models are useful Workshop (2 hr) LO11
Week 10 Models 2: Ordinary Least Squares Workshop (1 hr) LO11 LO12
Spatial analysis Workshop (2 hr) LO1 LO10 LO12
Week 11 Equity Practical (1 hr) LO5 LO6 LO8 LO9
System Design for social equity Workshop (2 hr) LO6 LO8 LO11
Week 12 Life Cycle impacts of transit operations Lecture (1 hr) LO1 LO3 LO5
Environmental Sustainability Calculations Practical (2 hr) LO3 LO5 LO8
Week 13 Beyond the classroom Workshop (1 hr) LO6 LO9
Project Workshop Workshop (2 hr) LO9 LO12

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
  • LO12. 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

This section outlines changes made to this unit following staff and student reviews.

In response to feedback, the Python start-up will be supported with formative assessment.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.