Outline

Overview

This unit of study offers students an introduction to civil engineering data analysis using examples of real-world transport operations applications. Students will undertake focused study of a selection of highly influential texts in the field and develop skills to recreate, evaluate and improve upon these seminal analyses using locally relevant datasets. 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
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	(MATH1005 or MATH1062) 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	Yes

Teaching staff

Coordinator	Benjy Marks, benjy.marks@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Experimental design	Project proposal Write a proposal that outlines your vision for a project and your specific role within it. This proposal should demonstrate that you understand the technical landscape, have thought through feasibility, and have a clear plan for execution.	20%	Week 03	3-4 page written document including visu	AI allowed
Outcomes assessed:
Oral test	Mid-project demonstration Demonstrate your working prototype and submit a progress report documenting what you've built so far. The demo should show a functional (even if incomplete) system that processes real transit data and produces some form of output.	20%	Week 07	20 minutes (oral)	AI prohibited
Outcomes assessed:
Oral exam	Final deliverable 20 minute live demonstration + 10 minutes of questions	30%	Week 13	30 minutes	AI prohibited
Outcomes assessed:
Written work	Final report Write a comprehensive individual report that reflects on the entire project. This report should demonstrate your personal understanding of the work, your specific contributions, and your learning throughout the semester.	30%	Week 13	8-10 page report	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Project proposal: Write a proposal that outlines your vision for a project and your specific role within it. This proposal should demonstrate that you understand the technical landscape, have thought through feasibility, and have a clear plan for execution.

Mid-project demonstration: Demonstrate your working prototype and submit a progress report documenting what you've built so far. The demo should show a functional (even if incomplete) system that processes real transit data and produces some form of output.

Final deliverable: Deliver a complete, working immersive visualization system that processes live transit data and projects meaningful analytics onto a physical surface. Your system should be reliable enough to run continuously during the showcase session.

Final report: Write a comprehensive individual report that reflects on the entire project. This report should demonstrate your personal understanding of the work, your specific contributions, and your learning throughout the semester.

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	Description
High distinction	85 - 100	Work that impresses and delights
Distinction	75 - 84	Correct, professionally presented, pertinent and insightful work
Credit	65 - 74	Correct, professionally presented and pertinent work
Pass	50 - 64	Correct work
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Project Initiation	Workshop (3 hr)
Week 02	Exploration & Scoping	Workshop (3 hr)
Week 03	Proposal Development	Workshop (3 hr)
Week 04	Foundation Building	Workshop (3 hr)
Week 05	Data Processing	Workshop (3 hr)
Week 06	Physical Setup	Workshop (3 hr)
Week 07	Mid-Project Checkpoint	Workshop (3 hr)
Week 08	Hardware Integration	Workshop (3 hr)
Week 09	Spatial Mapping	Workshop (3 hr)
Week 10	User Experience	Workshop (3 hr)
Week 11	Refinement & Optimisation	Workshop (3 hr)
Week 12	Final Integration	Workshop (3 hr)
Week 13	Project Showcase	Workshop (3 hr)

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

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.

Outcomes
Graduate qualities

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

LO1. Employ public transport terminology fluently
LO2. Perform calculations related to public transport planning and operations
LO3. Find and interpret literature to understand foundational transport concepts
LO4. Demonstrate mastery of foundational public transport concepts using informatics
LO5. Demonstrate understanding of the broader context for public transit including design, behavioural, regulatory, equity, economic and environmental considerations

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
Learning outcomes

Responding to student feedback

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

In 2026 this unit will be offered for the first time as a fully project-based unit of study where you will design and build a functional transport informatics system.

Additional information

Ed, the online platform that we use for coding.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

CIVL3704: Transport Informatics

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links