Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Civil Engineering School
This course introduces students to the field of transportation engineering and provides the fundamental knowledge required for the planning, design, and operation of transport systems to improve the mobility, safety, and reliability of road networks. Students will learn key principles and methodologies essential for understanding traffic flow theory, traffic modelling, traffic signal control, and travel demand forecasting. Through a combination of theoretical instruction and practical computer exercises, students will develop the skills necessary to analyse, visualise, and interpret traffic data, design and manage intersections, and predict travel demand.
This course provides a comprehensive introduction to the principles and methods essential to transportation engineering. It is structured into four key modules, each focusing on distinct aspects of traffic and transportation systems. In Module 1, students will learn traffic flow theory basics. This module covers fundamental traffic flow parameters and their relationships, the concept of fundamental diagrams, and various traffic data collection and analysis methods. In Module 2, students will explore travel demand modelling and forecasting. This module introduces the Four-step travel demand model, which includes trip generation, trip distribution, mode split, and traffic assignment. Module 3 focuses on statistical and probabilistic models of vehicle arrival process, which include the Poisson and exponential distributions and queueing theory. Module 4 covers the design and operations of various intersections, including unsignalised intersections, roundabouts, and signalised intersections, as well as traffic signal operations. Each module includes practical computer exercises utilising industry-standard software tools such as Python for programming, QGIS for geographic information systems (GIS), and SIDRA for intersection analysis. These hands-on exercises ensure that students not only understand theoretical concepts but also gain practical skills applicable to their future careers.
Course requirements
Recommended companion or co-requisite courses
We recommend completing the following courses at the same time:
CIVL2530
Incompatible
You can't enrol in this course if you've already completed the following:
CIVL2410
Course staff
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The course aims to enable students to:
- Develop an ability to analyse transportation systems by understanding interactions between travel demand and supply of transport services.
- Acquire basic knowledge and analytical skills for operating, planning, and designing safe and efficient transport services for the movement of people and goods.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Describe the interactions between infrastructure and human behaviour in transport systems, identify challenges the urban transport systems are facing, and explain the role of transport and traffic engineering
LO2.
Explain the key parameters of vehicular traffic flow and their relationships using the fundamental diagram.
LO3.
Apply statistical and probabilistic models and queueing theory to analyse vehicle arrival processes and delays.
LO4.
Describe the methods to collect, analyse, and visualise traffic data for the purpose of transport planning and traffic management.
LO5.
Explain the basic principles of the design and operation of signalised and unsignalised intersections and roundabouts.
LO6.
Apply the four-step process of trip generation, trip distribution, modal split and traffic assignment to forecast travel demand.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation |
Computer Exercise Submission
|
20% |
Week 2 Computer Exercise 8/08/2025 5:00 pm Week 4 Computer Exercise 22/08/2025 5:00 pm Week 5 Computer Exercise 29/08/2025 5:00 pm Week 6 Computer Exercise 5/09/2025 5:00 pm Week 7 Computer Exercise 12/09/2025 5:00 pm Week 8 Computer Exercise 19/09/2025 5:00 pm Week 9 Computer Exercise 26/09/2025 5:00 pm Week 10 Computer Exercise 10/10/2025 5:00 pm Week 11 Computer Exercise 17/10/2025 5:00 pm Week 12 Computer Exercise 24/10/2025 5:00 pm Week 13 Computer Exercise 31/10/2025 5:00 pm |
| Quiz |
In-class Quizzes
|
35% |
Week 2 Quiz 8/08/2025 Week 4 Quiz 22/08/2025 Week 5 Quiz 29/08/2025 Week 6 Quiz 5/09/2025 Week 7 Quiz 12/09/2025 Week 8 Quiz 19/09/2025 Week 9 Quiz 26/09/2025 Week 10 Quiz 10/10/2025 Week 11 Quiz 17/10/2025 Week 12 Quiz 24/10/2025 Week 13 Quiz 31/10/2025 |
| Examination |
Final Exam
|
45% |
End of Semester Exam Period 8/11/2025 - 22/11/2025 |
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
Computer Exercise Submission
- Online
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 20%
- Due date
Week 2 Computer Exercise 8/08/2025 5:00 pm
Week 4 Computer Exercise 22/08/2025 5:00 pm
Week 5 Computer Exercise 29/08/2025 5:00 pm
Week 6 Computer Exercise 5/09/2025 5:00 pm
Week 7 Computer Exercise 12/09/2025 5:00 pm
Week 8 Computer Exercise 19/09/2025 5:00 pm
Week 9 Computer Exercise 26/09/2025 5:00 pm
Week 10 Computer Exercise 10/10/2025 5:00 pm
Week 11 Computer Exercise 17/10/2025 5:00 pm
Week 12 Computer Exercise 24/10/2025 5:00 pm
Week 13 Computer Exercise 31/10/2025 5:00 pm
- Learning outcomes
- L02, L03, L04, L05, L06
Task description
Each week, students will be given a computer exercise task and asked to upload their completed files to Blackboard. The detailed instructions and due dates will be given in the task description.
A total of 11 computer exercises will be conducted between week 2 and week 13, and the best 10 scores will be counted toward the final grade. Each of these 10 exercises is worth 2%, making the total weight of the in-class quiz component 20%.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
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
- 35%
- Due date
Week 2 Quiz 8/08/2025
Week 4 Quiz 22/08/2025
Week 5 Quiz 29/08/2025
Week 6 Quiz 5/09/2025
Week 7 Quiz 12/09/2025
Week 8 Quiz 19/09/2025
Week 9 Quiz 26/09/2025
Week 10 Quiz 10/10/2025
Week 11 Quiz 17/10/2025
Week 12 Quiz 24/10/2025
Week 13 Quiz 31/10/2025
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
Each week, an in-class 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 11 quizzes will be conducted between week 2 and week 13, and the best 7 scores will be counted toward the final grade. Each of these 7 quizzes is worth 5%, making the total weight of the in-class quiz component 35%.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use 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.
Refer to School guidelines for procedure to apply for exemption. Link to School Guidelines is in the Policies and Procedures tab of the course profile.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Only the best 7 of 11 in-class quizzes will contribute to the mark of this assessment item.
Final Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 45%
- Due date
End of Semester Exam Period
8/11/2025 - 22/11/2025
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
The final exam will include a combination of problem solving questions.
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
To receive an overall grade of 4 or more, a student must achieve at least 40% on the final exam.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 - 19.99 |
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 - 44.99 |
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 - 49.99 |
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 - 64.99 |
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 - 74.99 |
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 - 84.99 |
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.
Supplementary assessment
Supplementary assessment is available for this course.
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
Library resources are available 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 |
Lecture |
Lectures Learning outcomes: L01, L02, L03, L04, L05, L06 |
Multiple weeks From Week 2 To Week 13 |
Tutorial |
Tutorial Sessions Tutorial sessions to be held throughout the semester. Learning outcomes: L01, L02, L03, L04, L05, L06 |
IT Computing |
Computer Exercise Sessions Computer exercise tasks are to be completed throughout the semester. A number of different software tools will be used in this course (e.g., Python, QGIS, and SIDRA). Learning outcomes: L02, L03, L04, L05, L06 |
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 for 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: