Course coordinator
Associate Professor Jiwon Kim
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Civil Engineering School
The interaction between demand (i.e., whether to travel or not, which route to take) and supply (i.e., roadway capacity, travel time) in the transport networks is a complex phenomenon that requires special analysis techniques. This course will expose students to the basic concepts of static and dynamic transport network analysis, as well as explore some applications. These models are used to model traffic flows at the scale of a city or metropolitan region, and are useful for predicting the impact of changes to the transportation system (added capacity, signal retiming, multimodal).
This course introduces students to the basic concepts and methods of transportation network modelling, providing a foundational understanding essential for urban transport planning and operations. The methods presented also serve as the basis for developingﾠtraffic simulation models widely used in analysingﾠand predicting traffic conditions in urban transportation networks. Topics covered include graph representation of road networks, graph theory and algorithms, equilibrium analysis of transportation systems, traffic assignment problems, travellers' choice behaviours, and discrete choice modelling. Throughout the course, students will engage in hands-on exercises to reinforce their understanding of transportation network modelling principles and their application in real-world scenarios.
A key component of the course is the application of Python programming for data analysis and algorithm implementation. Students will work extensively with Python data structures, pandas DataFrames, for-loops, if-statements, and function definitions to develop and analyse transport network models. A solid understanding of these fundamental programming skills is essential, and students without them will find the course challenging.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
CIVL2420
Course contact
Course staff
Lecturer
Associate Professor Jiwon Kim
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The course aims to enable students to:
- Use equilibrium analysis to study urban transportation systems.
- Develop a sound understanding on various optimization models and formulations of traffic network problems.
- Build the capability of applying network analysis tools to solve real-world transportation problems.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain the basic concepts of transport network modelling and its applications in transport planning and traffic operations.
LO2.
Formulate and solve basic static traffic assignment problems using mathematical programming and optimisation models
LO3.
Describe graph theory and algorithms for traffic assignment problems and network traffic analysis
LO4.
Formulate discrete choice models for route choice applications and explain traveller behaviours
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set | Assignment 1 - Problem Set/s | 15% |
28/04/2025 4:00 pm |
| Tutorial/ Problem Set | Assignment 2 - Problem Set/s | 15% |
26/05/2025 4:00 pm |
| Quiz |
In-class Quizzes
|
30% |
Week 4 In-class Quiz 18/03/2025 1:00 pm Week 5 In-class Quiz 25/03/2025 1:00 pm Week 6 In-class Quiz 1/04/2025 1:00 pm Week 7 In-class Quiz 8/04/2025 1:00 pm Week 8 In-class Quiz 15/04/2025 1:00 pm Week 9 In-class Quiz 29/04/2025 1:00 pm Week 11 In-class Quiz 13/05/2025 1:00 pm Week 12 In-class Quiz 20/05/2025 1:00 pm |
| Examination |
Final Examination
|
40% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Assignment 1 - Problem Set/s
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 15%
- Due date
28/04/2025 4:00 pm
- Learning outcomes
- L01, L02, L03
Task description
This assignment is designed to assess students' understanding of traffic assignment problems covered in lectures and their ability to apply practical skills learned in tutorials to solve relevant problems. The assignment problems will include both problem-solving questions and coding tasks in Python.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Assignments in this course can be extended by a maximum of one week.
Submission guidelines
Assessment must be submitted through the course BlackBoard site
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment 2 - Problem Set/s
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 15%
- Due date
26/05/2025 4:00 pm
- Learning outcomes
- L01, L04
Task description
This assignment is designed to assess students' understanding of discrete choice modelling covered in lectures and their ability to apply practical skills learned in tutorials to solve relevant problems. The assignment problems will include both problem-solving questions and coding tasks in Python.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Assignments in this course can be extended by a maximum of one week.
Submission guidelines
Assessment must be submitted through the course BlackBoard site
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
In-class Quizzes
- In-person
- Mode
- Written
- Category
- Quiz
- Weight
- 30%
- Due date
Week 4 In-class Quiz 18/03/2025 1:00 pm
Week 5 In-class Quiz 25/03/2025 1:00 pm
Week 6 In-class Quiz 1/04/2025 1:00 pm
Week 7 In-class Quiz 8/04/2025 1:00 pm
Week 8 In-class Quiz 15/04/2025 1:00 pm
Week 9 In-class Quiz 29/04/2025 1:00 pm
Week 11 In-class Quiz 13/05/2025 1:00 pm
Week 12 In-class Quiz 20/05/2025 1:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
In selected weeks (Weeks 3–12), an in-person quiz will be conducted during the tutorial session to assess students' understanding of the material covered in lectures and tutorials so far. The quiz will consist of a set of questions designed to reinforce key concepts and provide ongoing feedback on students' progress. Students are required to complete and submit their responses during the tutorial session on-site.
A total of eight quizzes will be conducted, and the best six scores will be counted toward the final grade. Each of these six quizzes is worth 5%, making the total weight of the in-class quiz component 30%.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Students are required to complete and submit their responses during the tutorial session on-site.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
The solution to each quiz is released within one week after the submission deadline.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Students need to complete the best 6 of 8 in-class quizzes.
Final Examination
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04
Task description
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
Students need to achieve 40% or higher in the final exam to pass the whole course.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. Course grade description: The student fails to demonstrate sufficient knowledge or understanding of the underlying concepts. Much of the information provided is inaccurate and irrelevant. Assessment is incomplete. |
| 2 (Fail) | 20 - 44 |
Minimal evidence of achievement of course learning outcomes. Course grade description: The student fails to demonstrate sufficient knowledge or understanding of the underlying concepts. Much of the information provided is inaccurate and irrelevant. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: Falls short of satisfying all the requirements for a Pass. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: The student demonstrates sound knowledge and at least partial understanding of the underlying concepts. Has some correct and some incorrect information. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: The student demonstrates sound knowledge and sound understanding of the key concepts. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: Key concepts are understood. There is a demonstrated ability to solve previously unseen problems. There are only minor factual inaccuracies and there is little irrelevant information. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Key concepts are understood and can be used to solve previously unseen problems. There are insignificant factual inaccuracies and there is very limited irrelevant information. |
Additional course grading information
Students need to achieve 40% or higher in the final exam to pass the whole course.
Grade cutoffs and hurdles
Final (total) marks will be rounded up to an integer value prior to applying hurdles or grade boundaries.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Where a student has been granted supplementary assessment and the form of assessment is a written examination held during the University's designated deferred and supplementary examination period the final grade awarded will be based on the results of the supplementary examination only, and a passing grade for the course will be awarded only if the student receives a passing grade or mark on the supplementary examination/s. The pass mark for the supplementary exam will be 50%.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Tutorial |
Tutorial sessions Tutorial sessions to be held throughout the semester. Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lectures Learning outcomes: L01, L02, L03, L04 |
|
Not Timetabled |
Self-managed study Students are expected to spend at least six hours per week, in addition to the timetabled lectures and tutorial sessions, to adequately complete the coursework. Learning outcomes: L01, L02, L03, L04 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: