Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Civil Engineering School
The growing amount of transport data that are available passively and actively from a variety of systems and sensors requires increasing knowledge of quantitative methods for descriptive and predictive purposes. Moreover, the location information in these data is increasingly important because the spatial context plays an essential role in decision making and problem solving in a number of transport applications. Accordingly, this course provides the necessary knowledge of regression techniques, Geographic Information Systems, and data mining, with emphasis on the theory as well as the practice of big data analytics.
As data science moves towards big data analytics, and as transport datasets become larger and more diverse in the amount and type of information, this course provides students with the necessary skills and tools to begin their analysis of large datasets. Specifically, the course addresses both the theory and practice of quantitative analysis of these data, encompassing quantitative values as well as spatial attributes, that are the hallmarks of transport data and other data in engineering.
Course requirements
Assumed background
The student is expected to enter this course having already mastered material from the equivalent of an undergraduate probability and statistics course.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
STAT2201 or CIVL2530 or CIVL7505 or ENGG7518
Restrictions
Minimum 30 students or at Head of School's discretion
Course contact
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
Transport studies commonly require a variety of methods in order to draw meaningful conclusions about existing travel conditions and to estimate future conditions. This course introduces students to the foundational methods that may assist them to analyse various common sources of transport data, including fundamental statistics, econometrics, data mining, and geographic and spatial analysis. This course also provides opportunities to work directly with these methods on various transport data sets.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain and apply the basic principles of statistical methods for transport data analytics.
LO2.
Explain and apply the basic principles of spatial analysis with geographic information systems (GIS) for transport data analytics.
LO3.
Formulate and solve spatial transport problems with GIS techniques.
LO4.
Perform decision-making with large transport datasets and propose solutions on the basis of the results of quantitative and spatial methods.
LO5.
Report and present results of transport data analytics to decision makers working on transport solutions.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Project | Linear Regression Project | 20% |
28/03/2025 4:00 pm |
| Paper/ Report/ Annotation, Project | Spatial Analysis and GIS Project | 20% |
2/05/2025 4:00 pm |
| Paper/ Report/ Annotation, Project | Data Mining and Spatial Analysis Project | 20% |
30/05/2025 4:00 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
Linear Regression Project
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 20%
- Due date
28/03/2025 4:00 pm
- Learning outcomes
- L01, L04, L05
Task description
The first project will involve the description, analysis, manipulation, visualisation, and reporting of results, using the tools of linear regression.
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
All electronically submitted assessment items must be submitted through TurnItIn in BlackBoard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 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.
Spatial Analysis and GIS Project
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 20%
- Due date
2/05/2025 4:00 pm
- Learning outcomes
- L02, L03, L04, L05
Task description
The second project will involve the description, analysis, manipulation, visualisation, and reporting of results, using the tools of spatial analysis and geographic information systems.
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
All electronically submitted assessment items must be submitted through Turnitin, in BlackBoard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 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.
Data Mining and Spatial Analysis Project
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 20%
- Due date
30/05/2025 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The third project will involve the description, analysis, manipulation, visualisation, and reporting of results, using the tools of data mining and related spatial analysis.
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
All electronically submitted assessment items must be submitted through TurnItIn in BlackBoard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 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.
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, L05
Task description
A final examination will be held during the standard examination period at the end of the semester. This exam will consist of short answer and 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, students must complete the final examination with a minimum grade of 3 (45% or more).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 | Open Book examination |
| 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 understand basic statistics, modelling, and information systems concepts, and fails to apply them. |
| 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 of statistics, modelling and information systems. Much of the communication on these concepts from the student is inaccurate or not relevant. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: The student evidences some knowledge of the subject material, but there is limited understanding of the underlying concepts of statistics, modelling, or information systems. A substantial part of the communication from the student on the material is inaccurate or not relevant. |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: The student demonstrates a sound knowledge of the underlying concepts of statistics, modelling, and information systems. The student communicates with some accuracy and relevance to specific information on the subject. To receive an overall grade of 4 or more, students must pass the assignment component (passing the assignments as a whole and not each individual assignment) with a grade of 3ﾠ(45% or more). |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: The student demonstrates a sound knowledge of the underlying concepts of statistics, modelling, and information systems. The student communicates with considerable accuracy, relevance, and fluency with specific information on the subject. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: The student demonstrates in-depth knowledge of the underlying concepts of statistics, modelling, and information systems. The student communicates with accuracy, relevance, and fluency with virtually all subject material. The student shows some ability to translate knowledge, skills and abilities to previously unseen problems. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: The student demonstrates mastery of the underlying concepts of statistics, modelling, and information systems. The student consistently communicates with accuracy, relevance, and fluency across all subject material. The student clearly demonstrates ability to translate knowledge, skills and abilities to previously unseen problems, and shows the ability to analyse those problems critically. |
Additional course grading information
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.
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.
Additional learning resources information
Additional course materials will be made available through Blackboard.
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 Activities Tutorials will be conducted in the one-hour sessions and will focus on practical applications using software and real-world data sets. Learning outcomes: L01, L02, L03, L04, L05 |
Lecture |
In-Class Lectures Lectures will include a combination of theory and practice, with in-class discussion. Learning outcomes: L01, L02, L03, L04, L05 |
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: