Outline

Overview

This unit provides a basic understanding of the main principles underlying strategic transport models for forecasting, and the knowledge to critically assess forecasts of transport strategies made by transport planners. Students acquire knowledge of strategic forecasting models used by government and consultants as well as the methods to capture travel behaviour such as mode choice and route choice. Simple mathematical models are discussed in detail, along with numerical examples and applications in the Sydney Metropolitan Area, which are used to illustrate the principles of the methods. This unit equips students to build simple transport models in the computer lab using specialised transport planning software used by governments and consultants.

Unit details and rules

Academic unit	Transport and Logistics Studies
Credit points	6
Prerequisites ?	None
Corequisites ?	ITLS5200 or ITLS6203 or TPTM6495
Prohibitions ?	TPTM6350
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Michiel Bliemer, michiel.bliemer@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
Written exam ?	Final exam Exam	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Written work	Travel demand modelling Report on travel demand modelling	30%	Week 08 Due date: 26 Sep 2025 at 23:59 Closing date: 06 Oct 2025	2500 words	AI allowed
Outcomes assessed:
Case studies	Case Study Case study report	30%	Week 13 Due date: 07 Nov 2025 at 23:59 Closing date: 17 Nov 2025	2500 words	AI allowed
Outcomes assessed:

Assessment summary

Travel demand modelling report: In this assessment, you analyse a hypothetical city in the OmniTRANS software, in which you will make forecasts for travel demand using aggregate transport modelling methods and conduct a simplified cost-benefit analysis. You are asked to write a brief report to the mayor of the city with your findings and recommendations.
Case study report: You are asked to investigate a possible extension of the train line to Bondi Beach and the impacts on the local community, both in the construction phase as well as the end phase. In groups of two, you will prepare a brief report advising the local community on the advantages and disadvantages, and also provide a critical assessment of your transport model.
Final exam: This exan consists of both multiple choice and open-ended questions. It will include key concepts in strategic transport planning, knowledge in the theory of aggregate and disaggregate travel demand modelling, theory of private and public transport assignment, as well as advanced transport modelling concepts.

Detailed information for each assessment can be found on Canvas.

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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
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.

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:

You will lose 5% of the maximum marks for assignments for every day late; assignments submitted more than 10 days after the formal due date will receive a mark of 0. Also, while exceeding the word limit by 10% is allowed, there is a penalty of 5% of the maximum marks for every additional 10% over the word limit.

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 transport and network modelling, data, and key concepts	Lecture (3 hr)
Week 01	Editing in OmniTRANS	Tutorial (3 hr)
Week 03	Aggregate demand modelling	Lecture (3 hr)
Week 03	Aggregate demand modelling in OmniTRANS	Tutorial (3 hr)
Week 05	Disaggregate demand modelling	Lecture (3 hr)
Week 05	Disaggregate demand modelling in Excel	Tutorial (3 hr)
Week 07	Private transport route choice and assignment	Lecture (3 hr)
Week 07	Private transport route choice and assignment in OmniTRANS	Tutorial (3 hr)
Week 09	Public transport route choice and assignment, bicycle assignment, pedestrian assignment	Lecture (3 hr)
Week 09	Public transport assignment in OmniTRANS	Tutorial (3 hr)
Week 11	Advanced transport models	Lecture (3 hr)
Week 11	Case study	Tutorial (3 hr)
Week 13	Q&A	Tutorial (3 hr)

Attendance and class requirements

Lecture recordings: All lectures and seminars are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

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.

Ortuzar J de D and Willumsen LG (2011) Modelling Transport, 4th edition.

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. identify and describe the different steps in the strategic transport modelling process
LO2. assess outcomes of strategic transport models and compare transport planning scenarios
LO3. model and analyse forecasts of travel behaviour and traffic using aggregate and disaggregate methods
LO4. communicate (in writing) the most relevant findings for specific target audiences.

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.

Lectures, tutorials and description of assessment tasks have been improved based on student feedback.

Additional information

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

Required readings

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

ITLS6102: Transport Modelling and Forecasting

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

Required readings

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