Outline

Overview

This unit of study aims to provide an introduction to the theory and practice of models and methods used for traffic operations. Topics include: queuing and traffic flow theory; traffic states; microscopic traffic models; fundamental diagrams; highway operation; ramp metering; congestion control; microscopic traffic simulation; transport data sources; unsignalized intersections and roundabouts; actuated and coordinated traffic signal control.

Unit details and rules

Academic unit	Civil Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	[(CIVL2700 or CIVL9700) or (MATH1021 and MATH1023 and MATH1005)] or [(MATH1061 and MATH1062) or (ENGG1801 or ENGG1810 or INFO1110)]. 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 work	Assignment 5 A set of calculation-based problems	6%	Formal exam period Due date: 08 Jun 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Written work	Project 3 - report Simulation-based project - Group Presentation and Group Report	15%	Formal exam period Due date: 15 Jun 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Written work	Assignment 1 Detailed annotated bibliography of a scholarly paper; Individual Report	5%	Week 05 Due date: 23 Mar 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Written work	Assignment 2 A set of calculation-based problems	4%	Week 06 Due date: 30 Mar 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Q&A following presentation, submission or placement	Project 1 - presentation Data analytics - Group Presentation and Q&A	12.5%	Week 07 Due date: 15 Apr 2026 at 23:59	15 minutes (with Q&A)	AI prohibited
Outcomes assessed:
Written work	Assignment 3 A set of calculation-based problems	10%	Week 08 Due date: 20 Apr 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Written work	Project 1 - report Data analytics - Group Presentation and Group Report	15%	Week 09 Due date: 27 Apr 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Q&A following presentation, submission or placement	Project 2 - presentation Simulation-based project - Group Presentation and Q&A (on Zoom)	15%	Week 11	15 minutes (with Q&A)	AI prohibited
Outcomes assessed:
Written work	Assignment 4 Detailed annotated bibliography of a scholarly paper; Individual Report	5%	Week 11 Due date: 11 May 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Q&A following presentation, submission or placement	Project 3 - presentation Simulation-based project - Group Presentation and Q&A	12.5%	Week 13 Due date: 27 May 2026 at 23:59	15 minutes (with Q&A)	AI prohibited
Outcomes assessed:
= group assignment ?

Assessment summary

Assignment 1:

Detailed annotated bibliography (individual short report) of one scholarly paper

Assignment 2:

A set of calculation-based problems

Assignment 3:

A set of calculation-based problems

Assignment 4:

Detailed annotated bibliography (individual short report) of one scholarly paper

Assignment 5:

A set of calculation-based problems

Project 1:

Traffic data analytics

In-class Presentation and discussion (group) + Report (group) (12.5% + 15%)

Project 2:

Implementation of ramp metering in microsimulation

(live) Presentation and discussion (group on zoom) (15%)

Project 3:

Intersection control in microsimulation

In-class Presentation and discussion (group) + Report (group) (12.5% + 15%)

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:

- If you need an extension for any of the assignments, you must submit a written request 48-hours before the due time and date outlining the reasons for requesting the extension and attaching supportive evidence such as a medical certificate. The request for an extension should be sent as an email to the Unit Coordinator. The email must be sent from your University email address. - Note that no assignment will be accepted once the solution has been returned to the students. - Assignments submitted electronically are due at 23:59 on the submission day. Assignment penalty for lateness is 5% per day. Assignments more than 10 days late or submitted once after the solutions are released on Canvas get 0. - Project reports are due at 23:59 on the submission day. Report penalties for lateness is 10% per day. Reports more than 5 days late 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 Traffic Engineering; Fundamentals of Traffic Flow Theory	Lecture (3 hr)
Week 02	Fundamentals of Traffic Flow Theory; Lab 1	Lecture (3 hr)
Week 03	Shock Waves in Traffic; Lab 1	Lecture (3 hr)
Week 04	Shock Waves in Traffic; Lab 1	Lecture (3 hr)
Week 05	Microscopic Traffic Models; Lab 1	Lecture (3 hr)
Week 06	Microscopic Traffic Models; Lab 1	Lecture (3 hr)
Week 07	Project 1 presentation; Lab demonstration	Seminar (3 hr)
Week 08	Motorway Traffic Management; Introduction to Microsimulation-Aimsun	Lecture (3 hr)
Week 09	Advanced Intersection Control; Lab 2	Lecture (3 hr)
Week 10	Lab 3 design and implementation (field data collection)	Practical (3 hr)
Week 11	Queueing Theory, Unsignalized Intersections; Lab 2&3	Lecture (3 hr)
Week 12	Unsignalized Intersections and Roundabouts; Lab 3	Lecture (3 hr)
Week 13	Project 3 presentations	Seminar (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. Searach and select technical documents, evaluate their reliability, usefulness, and relevance, and synthesise related content with help of AI
LO2. Demonstrate effective communication of solutions to multifaceted traffic problems through well-prepared reports
LO3. Function effectively and cooperatively within peer teams to deliver traffic engineering-related projects
LO4. Present ideas and the results of analyses in the appropriate language and terms for professional engineers
LO5. Practice quantitative traffic data collection trials and analyze them in traffic simulators
LO6. Associate the interplay between traffic flow theory and traffic engineering practice
LO7. Understand different traffic modelling approaches
LO8. Apply traffic flow and queueing theories to design and optimize traffic systems
LO9. Perform problem identification and formulation
LO10. Develop practical solutions for traffic problems based on the application of traffic engineering principles

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.

Assessments are spread out more throughout the semester. The lectures are delivered in a single weekly 3-hour session. Accordingly, the learning content has been edited and shuffled.

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

Disclaimer

On this page

Useful links

CIVL5702: Traffic Engineering

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links