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Unit outline_

CIVL5702: Traffic Engineering

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

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) AND (MATH1021 and MATH1023 and MATH1005) OR (MATH1061 and MATH1062) AND (ENGG1801 or ENGG1810). 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
Lecturer(s) Seyedehsan Seyedabrishami, seyedehsan.seyedabrishami@sydney.edu.au
The census date for this unit availability is 2 April 2024
Type Description Weight Due Length
Assignment group assignment Assignment 1
Annotated bibliography of a scholarly paper; Individual Report
5% Week 05
Due date: 18 Mar 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Assignment 2
A set of calculation-based problems
4% Week 06
Due date: 25 Mar 2024 at 23:59
n/a
Outcomes assessed: LO1 LO7 LO9
Creative assessment / demonstration group assignment Project 1
Data analytics - Group Presentation and Group Report
27.5% Week 07 n/a
Outcomes assessed: LO1 LO10 LO9 LO7 LO6 LO4 LO3 LO2
Assignment Assignment 3
A set of calculation-based problems
10% Week 08
Due date: 15 Apr 2024 at 23:59
n/a
Outcomes assessed: LO1 LO4 LO7 LO9
Presentation Project 2
Simulation-based project
15% Week 10 n/a
Outcomes assessed: LO1 LO10 LO9 LO8 LO7 LO6 LO5 LO4 LO2
Assignment group assignment Assignment 4
Annotated bibliography of a scholarly paper; Individual Report
5% Week 12
Due date: 13 May 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4
Creative assessment / demonstration group assignment Project 3
Simulation-based project - Group Presentation and Group Report
27.5% Week 13 n/a
Outcomes assessed: LO1 LO10 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Assignment 5
A set of calculation-based problems
6% Week 13
Due date: 26 May 2024 at 23:59
n/a
Outcomes assessed: LO1 LO4 LO8 LO9
group assignment = group assignment ?

Assessment summary

  • Assignment 1:

Annotated bibliography (individual short report) of one scholarly paper identified and analysed by the group

  • Assignment 2:

A set of calculation-based problems

  • Assignment 3:

A set of calculation-based problems

  • Assignment 4:

Annotated bibliography (individual short report) of one scholarly paper identified and analysed by the group

  • 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 (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.

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 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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 2023 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 2023. 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

WK Topic Learning activity Learning outcomes
Week 01 Opening Session; Introduction to Traffic Engineering; Fundamental of Traffic Flow Theory Lecture (4 hr) LO7
Week 02 Fundamentals of Traffic Flow Theory; Lab 1 Lecture and tutorial (4 hr) LO6 LO7
Week 03 Shock Waves in Traffic; Lab 1 Lecture and tutorial (4 hr) LO6 LO7 LO8
Week 04 Shock Waves in Traffic; Lab 1 Lecture and tutorial (4 hr) LO3 LO6 LO7 LO8 LO10
Week 05 Microscopic Traffic Models; Lab 1 Lecture and tutorial (4 hr) LO1 LO6 LO7
Week 06 Microscopic Traffic Models; Lab 1 Lecture and tutorial (4 hr) LO1 LO3 LO5 LO6 LO7 LO10
Week 07 Motorway Traffic Management; Project 1 presentation Lecture and tutorial (4 hr) LO4 LO5 LO6 LO7
Week 08 Introduction to Microsimulation-Aimsun; Lab 2 Lecture and tutorial (4 hr) LO1 LO5 LO6
Week 09 Advanced Intersection Control; Lab 2 Presentation (4 hr) LO3 LO5 LO6 LO8 LO9 LO10
Week 10 Lab 3; Lab 3 (field data collection) Lecture and tutorial (4 hr) LO3 LO6 LO8 LO9 LO10
Week 11 Queueing Theory, Unsignalized Intersections; Lab 3 Lecture and tutorial (4 hr) LO3 LO5 LO6 LO7 LO8 LO9 LO10
Week 12 Unsignalized Intersections and Roundabouts; Lab 3 Lecture and tutorial (4 hr) LO2 LO3 LO5 LO6 LO7 LO8 LO9 LO10
Week 13 Lab 3; Project 3 presentations Presentation (4 hr) LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10

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. Choose external information extracts, evaluate their reliability, usefulness, and relevance, and synthesise related content
  • 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 practice
  • LO7. Understand different traffic modelling approaches
  • LO8. Apply traffic flow and queueing theories to design and optimize traffic systems
  • LO9. Perform problem identification, formulation, and solution
  • 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Assessments are made more spread throughout the semester. The weightings of assessments are re-allocated based on students' feedback. Accordingly, learning contents have been edited and shuffled.

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.