ENN572 Artificial Intelligence in Transport
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | ENN572 |
|---|---|
| Prerequisite(s): | Admission to (EV51 OR EN51 OR EN61 OR EN71 OR EN75) |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,164 |
| Domestic tuition unit fee | $4,044 |
| International unit fee | $5,652 |
Unit Outline: Semester 2 2025, Gardens Point, Internal
| Unit code: | ENN572 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Admission to (EV51 OR EN51 OR EN61 OR EN71 OR EN75) |
| Coordinator: | Ashish Bhaskar | ashish.bhaskar@qut.edu.au |
Overview
The goal of smart transport systems is to increase safety, reduce traffic congestion, and its detrimental socio-economic and environmental impacts, and support to decarbonise the transport sector.
Recent advancements in artificial intelligence and technology provide new avenues to smartly achieve the above-mentioned goals.
This unit will introduce you to fundamentals of artificial intelligence and how AI enables the future of mobility. You will also grow your skills in techniques for advanced transport data analytics that can be applied to develop predictive models for transport operations and control in line with contemporary engineering practice.
Learning Outcomes
On successful completion of this unit you will be able to:
- Compare contemporary AI solutions for complex engineering problems
- Implement AI algorithms using appropriate tools and libraries
- Interpret client requirements for AI based solution to complex Transport system problem
- Select a preferred AI solution to a complex Transport system design problem, prepare a detailed design for that solution, and assess its impacts
- Analyse complex transport data sets to provide engineering recommendations for transport problems
Content
Learning in this unit is within two modules
Module-1: Introduction to applied Artificial Intelligence
- Fundaments of Artificial engineering
- Recent developments in AI
- Supervised, unsupervised and reinforcement learning
- Classification and clustering algorithms
Module-2: AI applications in Transport
- Applications of machine learning techniques in transport operations and control
- Applications of machine learning in transport modelling and simulation
AI applications of machine learning in emerging connected and autonomous vehicles
Learning Approaches
In this unit you can expect to experience the following activities:
- Lectures – 2 hours per week
- Tutorials/workshop – 1 hours per week
You will undertake problem-based learning tasks, in groups, emphasizing your ability of working as a team and your ability to communicate professionally.
Feedback on Learning and Assessment
During tutorial classes, with your group you will share your formative ideas for your AI in transport project and you will receive feedback from teaching and learning team. You are encouraged to view your group as a learning community and to share and discuss emergent ideas in the transport engineering process and your understandings of transport engineering professional practice. Each assessment submission will be graded against criteria and standards that will be shared with you at the beginning of semester through Assessment Task Descriptions and Marking Rubrics. Marked assessment will include feedback given by the markers against the criteria.
Assessment
Overview
Assessment in this unit has been designed to give you the opportunity to show your learning against the unit learning outcomes. You will work in a group to prepare and submit two assessments related to AI in Transport engineering during the semester, deploying work practices that align to transport engineering professional practice (such as project management, cultural perspectives). You will be expected to work together with your group members and independently to make individual contributions to the assessments.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: AI Architecture Recommendation
You will be presented with an engineering problem, and asked to compare and contrast AI Architectures that could be used to determine a solution, making a recommendation for implementation.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Transport AI Report
You will be presented with a complex transport engineering challenge. You will develop a report on a proposed AI solution to this challenge.
Assessment: Engineering Presentation
You will be required to formally and professionally present your solution to the engineering problem to the teaching team. Your presentation should include the proposed solution, scope assumptions, limitation and future work. It should demonstrate your understanding to the problem.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
The unit has computer based lab/workshop. Students are expected to be in the computer lab to complete the assessment. Risks associated with normal computer sessions are applicable here.
Risk Assessment Statement
The unit has computer based lab/workshop. Students are expected to be in the computer lab to complete the assessment. Risks associated with normal computer sessions are applicable here.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN51 Master of Sustainable Infrastructure
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices as they relate to contemporary practice in Sustainable Infrastructure
Relates to: AI Architecture Recommendation, Transport AI Report - Analyse and evaluate Sustainable Infrastructure problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: AI Architecture Recommendation - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Sustainable Infrastructure in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Transport AI Report - Effectively communicate Sustainable Infrastructure problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Transport AI Report, Engineering Presentation - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: AI Architecture Recommendation, Transport AI Report
EN56 Master of Engineering Technology
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices as they relate to contemporary practice in Engineering Technology
Relates to: AI Architecture Recommendation, Transport AI Report - Analyse and evaluate Engineering Technology problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: AI Architecture Recommendation - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Engineering Technology in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Transport AI Report - Effectively communicate Engineering Technology problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Transport AI Report, Engineering Presentation - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: AI Architecture Recommendation, Transport AI Report
EN61 Graduate Certificate in Smart Transport and Mobility
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary practice in Smart Transport & Mobility
Relates to: AI Architecture Recommendation, Transport AI Report - Analyse and evaluate Smart Transport & Mobility problems using technical approaches informed by contemporary practice to achieve innovative, critically informed solutions
Relates to: AI Architecture Recommendation - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Smart Transport & Mobility in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Transport AI Report - Effectively communicate Smart Transport & Mobility problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Transport AI Report, Engineering Presentation - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: AI Architecture Recommendation, Transport AI Report
EN71 Master of Sustainable Infrastructure with Project Management
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Sustainable Infrastructure and Project Management domains
Relates to: AI Architecture Recommendation, Transport AI Report - Analyse and evaluate problems in Sustainable Infrastructure and Project Management domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: AI Architecture Recommendation - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Sustainable Infrastructure and Project Management domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Transport AI Report - Effectively communicate problems in Sustainable Infrastructure and Project Management domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Transport AI Report, Engineering Presentation - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: AI Architecture Recommendation, Transport AI Report
EN75 Master of Sustainable Infrastructure with Data Analytics
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Sustainable Infrastructure and Data Analytics domains
Relates to: AI Architecture Recommendation, Transport AI Report - Analyse and evaluate problems in Sustainable Infrastructure and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: AI Architecture Recommendation - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Sustainable Infrastructure and Data Analytics domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Transport AI Report - Effectively communicate problems in Sustainable Infrastructure and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Transport AI Report, Engineering Presentation - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: AI Architecture Recommendation, Transport AI Report
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: AI Architecture Recommendation, Transport AI Report - Analyse and evaluate Engineering problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: AI Architecture Recommendation - Apply innovative, systematic approaches to plan, design, deliver and manage Engineering projects in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Transport AI Report - Effectively communicate Engineering problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Transport AI Report, Engineering Presentation - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: AI Architecture Recommendation, Transport AI Report