ENN572 Artificial Intelligence in Transport


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Unit Outline: Semester 2 2026, Gardens Point, Internal

Unit code:ENN572
Credit points:12
Pre-requisite:Admission to (EV51 OR EN51 OR EN56 OR EN61 OR EN71 OR EN75 OR EN80)
Coordinator:Sebastien Glaser | sebastien.glaser@qut.edu.au
Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of the teaching period.

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, AI, in a transport context, and how it 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:

  1. Compare contemporary AI solutions for complex engineering problems
  2. Implement AI algorithms using appropriate tools and libraries
  3. Interpret client requirements for AI based solution to complex Transport system problem
  4. Select a preferred AI solution to a complex Transport system design problem, prepare a detailed design for that solution, and assess its impacts
  5. 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:

    • Engage with timetabled learning activities on campus and ask questions.
      • Lectures – 2 hours per week – From experienced professional civil engineers, researchers, providing insights into knowledge, skills and applicative domain.
      • Tutorials/workshop – 1 hours per week – providing opportunities for individual or group works for problem solving and capacity development on real examples.

       

      These activities will be detailed by week of semester on the unit schedule. You can also expect to be provided with learning resources including video presentations and readings on a unit Canvas site, which you can access flexibly to complete your learning in this unit.

       

      At the beginning of the unit, you will be made aware of the ways in which you can ask questions or seek clarification from the unit coordinator.

       

      You are expected to:

    • Engage with timetabled learning activities on campus and ask questions.

    • Engage with online resources outside of timetabled learning activities. They will be available on the unit Canvas site. You will receive regular email announcements regarding the release of these resources.

    • You will undertake problem-based learning tasks, in groups, emphasizing your ability of working as a team and your ability to communicate professionally.
    • Prepare for learning activities according to the unit schedule and follow up on any work not completed.
    • Complete assessment tasks by working consistently throughout the semester and meeting the due dates that are published via the unit Canvas site.

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. During the semester you will work in a group to prepare and submit two assessments related to civil engineering systems analysis during the semester, deploying work practices that align to civil engineering professional practice (including sustainability and industry relevant digital practices and technologies). You will be expected to work together with your group, as a leader and as a group member, and independently to make individual contributions to the assessments. You will sit an examination individually during the central examination period at the end of semester, where you will show your overall learning in the unit.

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.

The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.

Weight: 40
Length: 30 pages
Individual/Group: Individual and group
Due (indicative): Week 6
Related Unit learning outcomes: 1, 2

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.

The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Weight: 40
Length: 30 pages
Individual/Group: Individual and group
Due (indicative): Week 12
Related Unit learning outcomes: 5

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.

  • This is a verified identity assessment where students will complete and submit their work in class.
  • The use of generative artificial intelligence (GenAI) tools is prohibited in this assessment.
  • Attendance is compulsory in this assessment. In case of non-attendance, zero marks will be awarded for this assessment. If the attendance is missed due to unforeseen personal circumstances, you can apply for special consideration with the required documentation. 
Weight: 20
Individual/Group: Individual
Due (indicative): During central examination period
Central exam duration: 3:10 - Including 10 minute perusal
Related Unit learning outcomes: 1, 2, 3, 4, 5

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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

  1. Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
    Relates to: AI Architecture Recommendation, Transport AI Report
  2. 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
  3. 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
  4. 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
  5. 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