Outline

Overview

This unit of study aims to provide an introduction to transport systems and is assumed knowledge for units on traffic engineering, transport planning, and city logistics. Topics include: the role of accessibility as the reason for transport; the history of transport technologies in Australia and globally; the characteristics of the principle modes of transport; factors behind the demand for mobility; qualitative choice modeling; agent-based modeling; predicting travel demands; the mechanics of queueing and traffic flow; intelligent transport systems; the microscopic and macroscopic fundamental diagrams; highway capacity and level of service; the design of transport junctions.

Unit details and rules

Academic unit	Civil Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Basic statistics through regression analysis, differential and integral calculus, computer programming
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Mohsen Ramezani, mohsen.ramezani@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam Evaluate the learning outcomes for the whole semester	40%	Formal exam period	2.5 hours	AI prohibited
Outcomes assessed:
Written work	Assignment 4 Final Group Assignment. SparkPlus may be used to assess contributions.	9%	Formal exam period Due date: 08 Jun 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Out-of-class quiz	Self-assessment quiz Online Canvas quiz	0%	Week 03 Due date: 15 Mar 2026 at 23:59 Closing date: 15 Mar 2026	10 minutes	AI allowed
Outcomes assessed:
Written work	Assignment 1 Solve Problems relating to the content from the first few weeks.	7%	Week 06 Due date: 30 Mar 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Written test	In-class quiz 1 In-class quiz assessing the lecture topics of weeks 1 to 4	15%	Week 07 Due date: 15 Apr 2026 at 11:00	80 minutes	AI prohibited
Outcomes assessed:
Written work	Assignment 2 Solve Problems relating to the content from the middle of the semester.	7%	Week 09 Due date: 27 Apr 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Written test	In-class quiz 2 In-class quiz assessing the lecture topics of weeks 5 to 8	15%	Week 10 Due date: 06 May 2026 at 11:00	80 minutes	AI prohibited
Outcomes assessed:
Written work	Preliminary report of group project A preliminary report to get feedback on Assignment 4	0%	Week 11 Due date: 11 May 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Written work	Assignment 3 Solve Problems relating to the content from the end of the semester.	7%	Week 13 Due date: 25 May 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ? = early feedback task ?

Assessment summary

There may be statistically and educationally defensible methods used when combining the marks from each component to ensure consistency of marking between markers, and alignment of final grades with unit outcomes and grade descriptors.
The University has authorised and mandated the use of text-based similarity detecting software Turnitin for all text-based written assignments. Detailed information for each assessment can be found on Canvas
Detailed information for each assessment can be found on Canvas.
The final exam is a hurdle. Students must achieve 40% on the final exam to pass.

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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Assignments submitted electronically are due at 23:59 on the submission day. Assignment penalties for lateness is 5% per day. Assignments more than 10 days late or submitted once after the solutions are released on Canvas get 0.

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	Opening Session; Introduction to Transport Systems	Lecture (4 hr)
Week 01	Tutorial - Discussion	Tutorial (2 hr)
Week 02	Transport Network Modelling (Traffic Assignment)	Lecture (4 hr)
Week 02	Tutorial - Problem Solving	Tutorial (2 hr)
Week 03	Choice Modelling	Lecture (4 hr)
Week 03	Tutorial - Problem Solving	Tutorial (2 hr)
Week 04	Travel Demand Forecasting	Lecture (4 hr)
Week 04	Tutorial - Problem Solving	Tutorial (2 hr)
Week 05	Departure Time Modelling; Queueing Theory	Lecture (4 hr)
Week 05	Tutorial - Problem Solving	Tutorial (2 hr)
Week 06	Queueing Theory; Fundamental of Traffic Engineering	Lecture (4 hr)
Week 06	Tutorial - Problem Solving	Tutorial (2 hr)
Week 07	Fundamental of Traffic Engineering; Traffic States	Lecture (4 hr)
Week 07	Tutorial - Problem Solving	Tutorial (2 hr)
Week 08	Traffic States and Measurements; Macroscopic Traffic Models	Lecture (4 hr)
Week 08	Tutorial - Problem Solving	Tutorial (2 hr)
Week 09	Principles of Intersection Control	Lecture (4 hr)
Week 09	Tutorial - Problem Solving	Tutorial (2 hr)
Week 10	Traffic Signal Timing Design and Analysis	Lecture (4 hr)
Week 10	Tutorial - Problem Solving	Tutorial (2 hr)
Week 11	Public Transport Operation and Planning	Lecture (4 hr)
Week 11	Tutorial - Problem Solving	Tutorial (2 hr)
Week 12	Public Transport Operation and Planning	Lecture (4 hr)
Week 12	Tutorial - Problem Solving	Tutorial (2 hr)
Week 13	Emerging Transport Technologies; Closing Session	Lecture (4 hr)
Week 13	Tutorial - Problem Solving	Tutorial (2 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.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Juan de Dios Ortúzar, Luis G. Willumsen, Modelling Transport (4th).
Fred L. Mannering, Scott S. Washburn, Principles of Highway Engineering and Traffic Analysis (5th).
Roger P. Roess, Elena S. Prassas, William R. McShane, Traffic Engineering (3rd).

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. Seek basic information to answer general transport queries using standard knowledge resources, e.g AI and library inquiries and web-based information
LO2. Evaluate reliability of external information extracts and synthesize relevant content
LO3. Function effectively as an individual in multidisciplinary and multicultural teams to deliver traffic related projects
LO4. Recognize that safety, efficiency, and sustainability are all crucial considerations for the design of transport systems
LO5. Explain the characteristics of the Australian transport infrastructure and identify the challenges it is facing
LO6. Distinguish the specific characteristics of each transport mode and identify the appropriate tool(s) for the needed analysis
LO7. Apply travel demand forecasting methods to predict motor vehicle, pedestrian, bicycle, and public transport flows given input land use, network geometry, and travel behaviour characteristics
LO8. Apply fundamental principles in the design of traffic controls
LO9. Demonstrate knowledge of the role that advanced technologies play and will play in the field of transport and traffic engineering
LO10. Recommend appropriate public transport service to serve new developments
LO11. Recommend appropriate measures for the design of a junction to serve all travelers and the community
LO12. Perform basic transport field data analysis
LO13. Undertake problem identification and formulation, and develop solutions

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.

Extra learning materials have been added for tutorial classes.

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

CIVL9700: Transport Systems

Semester 1, 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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links