Outline

Overview

This unit of study uses a hands-on, data-driven approach to exploring foundational concepts in transport. Students will develop skills to convert data into information for decision making including data ingestion, data description, visualisation, error analysis, and basic modelling. The data science skills will be taught using Python notebooks. The students will use an integrated approach, drawing on perspectives from multiple disciplines and exercising their judgement regarding social, environmental and economic sustainability. Mastery of the concepts will be demonstrated through submitted technical analysis as well as clear written, graphical and oral communication.

Unit details and rules

Academic unit	Civil Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	CIVL3704 or CIVL9704
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Emily Moylan, emily.moylan@sydney.edu.au
Lecturer(s)	Emily Moylan, emily.moylan@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Oral exam ?	Oral Exam Oral exam with skill demo and open questions.	20%	Formal exam period	15 minutes (oral)	AI prohibited
Outcomes assessed:
Contribution	Participation in class Contribution of insights and/or questions to class discussion	15%	Ongoing	4 hrs per week	AI allowed
Outcomes assessed:
Data analysis	Data and Coding Assignment Submitted code for ingesting, visualising and describing data	15%	Week 05 Due date: 05 Sep 2025 at 23:59	N/A	AI allowed
Outcomes assessed:
Written work	Prompt Engineering An iterative conversation with a large language model in order to refine ideas for the best approaches to achieve mode shift	20%	Week 08 Due date: 26 Sep 2025 at 23:59	2 pages	AI allowed
Outcomes assessed:
Evaluation	Project Peer Feedback Give and receive peer feedback on group projects	5%	Week 12 Due date: 30 Oct 2025 at 15:00	2 hrs	AI allowed
Outcomes assessed:
Research analysis	Final Project Group project building on the analysis presented in the unit	25%	Week 13 Due date: 07 Nov 2025 at 23:59	20 pages	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Class participation: The student will engage consistently with the class demonstrated through insightful contributions to class discussion and/or relevant questions asked in a setting that allows their classmates to benefit.
Data and coding Assignment: The student will build on the analysis accomplished in class to demonstrate their proficiency in transport analytics. The student will submit their code using the EdStem platform. The mark will be based on the code outputs as well as the students’ approach to the analysis as explained through comments in the code
Prompt Engineering: The students will use their learning from class discussions to engineer a prompt to a large language model to ideate a program of change to achieve transport mode shift.
Final Project: The students will work in groups to address a research question related to mode shift. The analysis should build on the work presented in class and address future trends in transport. The peer feedback supports the submission of the final project.
Oral Exam: Each student will present to the teaching staff. The student must demonstrate their ability to think algoirthmically in order to undertake transport analysis and to respond to open ended questions in a way that draws on literature, evidence and their values.

Assessment criteria

Result name	Mark range	Description
HD	85+	Student demonstrates rich insights into the material, creates novel analysis supported by data and code to critique foundational concepts, uses illuminating visual, written and oral communication.
D	75-84	Student demonstrates considered opinions on the material, uses imaginative data and code to evaluate foundational concepts, produces compelling visual, written and oral communication.
C	65-74	Student demonstrates sound understanding of the material, builds on past analysis using data and code to assess foundational concepts, communicates technical results persuasively with professional and polished visual, written and oral communication.
P	50-64	Student demonstrates comprehension of the material, reproduces past analysis, employs data and code to explore evidence for foundational concepts, communicates technical results using visual, written and oral outputs.
F	below 50	Student cannot demonstrate comprehension of the material, lacks technical skills to reproduce past analysis or build evidence through data and code, and/or produces unclear or misleading visual, written and oral communication.

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	Introduction to the UoS; Meeting your classmates	Lecture (2 hr)
Week 01	Writing code: Part A	Tutorial (2 hr)
Week 02	Why mode shift	Workshop (2 hr)
Week 02	Writing Code: Part B	Tutorial (2 hr)
Week 03	What is mode shift: Part A	Workshop (2 hr)
Week 03	Writing Code: Part C	Tutorial (2 hr)
Week 04	Assignment 1 Workshop	Workshop (2 hr)
Week 04	Writing Code: Part D	Tutorial (2 hr)
Week 05	Guest lecture	Lecture (2 hr)
Week 05	Writing Code: Part E	Tutorial (2 hr)
Week 06	Guest Lecture	Lecture (2 hr)
Week 06	Assignment 2 Workshop	Workshop (2 hr)
Week 07	How to achieve mode shift: Part A	Workshop (2 hr)
Week 07	How to achieve mode shift: Part B	Workshop (2 hr)
Week 08	How to achieve mode shift: Part C	Workshop (2 hr)
Week 08	How to achieve mode shift: Part D	Workshop (2 hr)
Week 09	Project Workshop	Workshop (2 hr)
Week 09	Who, when, where: Part A	Workshop (2 hr)
Week 10	Who, when, where: Part B	Workshop (2 hr)
Week 10	AI Concepts: Part A	Workshop (2 hr)
Week 11	Who, when, where: Part C	Workshop (2 hr)
Week 11	AI Concepts: Part B	Workshop (2 hr)
Week 12	Who, when, where: Part D	Workshop (2 hr)
Week 12	Peer feedback	Workshop (2 hr)
Week 13	Project help	Workshop (2 hr)
Week 13	Future transport trends	Workshop (2 hr)

Attendance and class requirements

This is an in-person unit with class participation marks based on vocalised engagement with the material and classmates.

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. Incorporate data science tools to analyse transport systems
LO2. Develop solutions to open-ended transport questions and support the solutions with evidence
LO3. Select and apply appropriate modelling techniques
LO4. Communicate understanding of the transport system through compelling oral, written, and graphical presentation
LO5. Contribute to multidisciplinary teams to deliver transport analysis
LO6. Apply values and judgment consistent with economic, social and environmental sustainability
LO7. Demonstrate integrated thinking across engineering, planning and business perspectives on transport

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.

This class has been substantially changed in response to student feedback. Past comments noted the low level of coding expertise in the class, and I have subsequently increased what is being taught. The focus here is on functional programming and the ability to interrogate code written by others or AI. Additionally, I have de-emphasised the use of the literature and refocused the content on a more policy-oriented topic to match the professional standing of many of our students.

Additional information

Ed Discussion

Disclaimer

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.

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

Attendance and class requirements

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

CIVL5704: Transport Analytics

Semester 2, 2025 [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

Attendance and class requirements

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