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Unit outline_

CIVL5704: Transport Analytics

Semester 2, 2021 [Normal day] - Remote

This unit of study uses a hands-on, data driven approach to exploring foundational concepts in transport. Students will undertake focussed study of a selection of highly influential texts in the field and develop skills to recreate, evaluate and improve upon these seminal analyses. The students will use an integrated approach, drawing on perspectives from multiple disciplines and exercising their judgement regarding social, environmental and economic sustainability. Mastery of the concepts will be demonstrated through submitted technical analysis as well as clear written and graphical communication.

Unit details and rules

Academic unit Civil Engineering
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

CIVL3704 OR CIVL9704

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Emily Moylan, emily.moylan@sydney.edu.au
Type Description Weight Due Length
Skills-based evaluation Oral Exam
Oral exam with skill demo and open questions.
10% Formal exam period 15 min
Outcomes assessed: LO1 LO7 LO5 LO4 LO3
Assignment Data and Coding Assignment
Submitted code for analysing data
15% Week 05 N/A
Outcomes assessed: LO1 LO2 LO4 LO5
Assignment Model Memo
Written and graphic communication of a model formatted as a memorandum
25% Week 08 2 pages
Outcomes assessed: LO1 LO2 LO4 LO5
Assignment Project Peer Feedback
Give and receive peer feedback on group projects
5% Week 11
Due date: 08 Nov 2020 at 23:59
1 week
Outcomes assessed: LO5 LO6
Assignment group assignment Final Project
Group project building on the analysis presented in the unit
45% Week 13 20 pages
Outcomes assessed: LO1 LO2 LO3 LO5 LO6 LO7
group assignment = group assignment ?

Assessment summary

  • Data and coding Assignment: The student will build on the analysis accomplished in class to demonstrate their proficiency in comprehending the literature, applying models to problems, and working with real data. The student will submit their code using the EdStem platform. The mark will be based on the code outputs as well as the students’ approach to the analysis as explained through comments in the code
  • Model Memo: The students will code and run a model that was presented in the literature. The model will be presented in the format of a memorandum using written and graphical communication to explain the motivation for the model, its strengths and weaknesses and potential extensions. 
  • Final Project: The students will work in groups to address a research question related to a seminal transport text. The analysis should build on the work presented in class and address future trends in transport. 
  • Oral Exam: Each student will present to the teaching staff using an online coding environment and screensharing software. The student must demonstrate their ability to undertake transport analysis using code and to respond to open ended questions in a way that draws on literature, evidence and their values. 

Assessment criteria

Result name Mark range Description
HD 85+ Student demonstrates rich insights into seminal texts, creates novel analysis supported by data and code to critique foundational concepts, uses illuminating visual, written and oral communication.
D 75-84 Student demonstrates considered opinions of seminal texts, uses imaginative data and code to evaluate foundational concepts, produces compelling visual, written and oral communication. 
C 65-74 Student demonstrates sound understanding of seminal texts, builds on past analysis using data and code to assess foundational concepts, communicates technical results persuasively with professional and polished visual, written and oral communication.
P 50-64 Student demonstrates comprehension of seminal texts, reproduces past analysis, employs data and code to explore evidence for foundational concepts, communicates technical results using visual, written and oral outputs.
F below 50 Student cannot demonstrate comprehension of the seminal texts, lack technical skills to reproduce past analysis or build evidence through data and code, and/or produce unclear or misleading visual, written and oral communication.

 

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to the UoS; Meeting your classmates Lecture (2 hr) LO4 LO6
Writing code: Part A Lecture and tutorial (2 hr) LO2
Week 02 Literature reading guide; Text 1: Context, application, impact Lecture (2 hr) LO2 LO4
Writing Code: Part B Tutorial (2 hr) LO2
Week 03 Solving problems algorithmically Tutorial (2 hr) LO1 LO4
Writing Code: Part C Tutorial (2 hr) LO2 LO5
Week 04 Implementing a model in code Tutorial (2 hr) LO1
Introduce Project 1, a toy network Lecture and tutorial (2 hr) LO1 LO5
Week 05 Improving and communicating your analysis Tutorial (2 hr) LO3 LO5 LO7
Policy implications of Text 1 Lecture (2 hr) LO1 LO3 LO7
Week 06 Text 2: context, application and impact Lecture (2 hr) LO1 LO3 LO7
Implementing a model in code Tutorial (2 hr) LO1
Week 07 Implementing a model in code without reinventing the wheel Tutorial (2 hr) LO1 LO4
Memo workshop Workshop (2 hr) LO1 LO3 LO5
Week 08 Policy implications of Text 2 Lecture (2 hr) LO1 LO3 LO7
Week 09 Text 3: Context, application and impact Lecture (2 hr) LO1 LO3
Groupwork workshop Workshop (2 hr) LO3 LO6
Week 10 Implementing a model in code Tutorial (2 hr) LO1 LO2
Exploring the potential of an idea Lecture and tutorial (2 hr) LO1 LO3 LO6 LO7
Week 11 GIS Tutorial (2 hr) LO2 LO5
Visualising evidence for a concept Tutorial (2 hr) LO2 LO5
Week 12 Policy implications of Text 3 Lecture (2 hr) LO3 LO7
Peer feedback on groupwork Workshop (2 hr) LO5 LO6
Week 13 Project help Workshop (2 hr) LO5 LO6
Future transport trends Lecture (2 hr) LO1 LO3 LO7

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Apply and critique foundational models and theories in transport
  • LO2. Demonstrate mastery of foundational transport concepts using data analysis
  • LO3. Demonstrate integrated thinking across engineering, planning and business perspectives on transport
  • LO4. Find and interpret literature to understand foundational transport concepts
  • LO5. Communicate understanding of the transport system through compelling oral, written, graphic presentation
  • LO6. Contribute to multidisciplinary teams to deliver transport projects
  • LO7. Apply values and judgment consistent with economic, social and environmental sustainability

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

Alignment with Competency standards

Outcomes Competency standards
LO1
Stage 1 Competency Standard for Professional Engineer (UG) - EA
1.1 (L3). Scientific knowledge. (Level 3- Exceeding required standard) Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.
LO2
Stage 1 Competency Standard for Professional Engineer (UG) - EA
1.3 (L3). Specialist discipline knowledge. (Level 3- Exceeding required standard) In-depth understanding of specialist bodies of knowledge within the engineering discipline.
1.4 (L2). Discipline research knowledge. (Level 2- Attaining required standard (Bachelor Honours standard AQF8)) Discernment of knowledge development and research directions within the engineering discipline
2.1 (L3). Complex problem-solving. (Level 3- Exceeding required standard) Application of established engineering methods to complex engineering problem solving
2.2 (L3). Use of engineering techniques, tools and resources. (Level 3- Exceeding required standard) Techniques, tools and resources.

This section outlines changes made to this unit following staff and student reviews.

Based on last year's feedback, more time has been allocated to creating a foundation of Python skills at the start of the class. This is helped by the change from a 12 to 13 week delivery.

Work, health and safety

This unit can be taken fully online, but meetings should be attended synchronously. 

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.