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We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

Unit of study_

CIVL3704: Transport Informatics

This unit of study offers students an introduction to civil engineering data analysis using examples of real-world transport operations applications. Students will develop skills to convert data into information for decision making including data ingestion, data structures, summarisation, visualisation, error analysis, and basic modelling. The data science skills will be taught using Python notebooks. In parallel with data science skills, this unit of study will introduce public transport system operations and planning. Lecture and reading content will provide a foundation of history, terminology and methods to assess the performance of public transport systems and make data-driven planning decisions. The datasets will be drawn from urban public transport applications, and explore real-world challenges in transport informatics.

Details

Academic unit Civil Engineering
Unit code CIVL3704
Unit name Transport Informatics
Session, year
? 
Semester 1, 2020
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
? 
ENGG2851
Prerequisites
? 
None
Corequisites
? 
None
Assumed knowledge
? 

MATH1005 AND CIVL2700. Understanding of statistical inference. Familiarity with the urban transport network and basic concepts in transport studies.

Available to study abroad and exchange students

Yes

Teaching staff and contact details

Coordinator Emily Moylan, emily.moylan@sydney.edu.au
Type Description Weight Due Length
Assignment Reading quizzes
Online quizzes
5% - n/a
Outcomes assessed: LO2 LO3 LO4 LO5 LO7 LO10
Assignment Comprehension quizzes
Online quizzes
5% - n/a
Outcomes assessed: LO2 LO3 LO4 LO7 LO8 LO10
Assignment Problem sets
Problem sets on Ed due Week 3, 5
20% - n/a
Outcomes assessed: LO1 LO2 LO6 LO8 LO10 LO11 LO12
Assignment Project: Proposal
Short proposal for the data collection project
5% Week 06 n/a
Outcomes assessed: LO6 LO9 LO11
Assignment Project: visualising and presenting information
Submission of visualisation plus peer feedback
15% Week 07 n/a
Outcomes assessed: LO1 LO2 LO6 LO8 LO9 LO10 LO11 LO12
Assignment Project: collecting and analysing data
Report on data collection, analysis and interpretation
35% Week 11 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO9 LO10 LO11 LO12
Assignment Project: Spatial Supplement
Creation of maps and analysis
15% Week 13 n/a
Outcomes assessed: LO1 LO2 LO6 LO8 LO9 LO10 LO11 LO12
  • Reading quizzes: Short online quizzes accompany the provided readings. The mark is made of 10 equally weighted quizzes.
  • Comprehension quizzes: Short online quizzes accompany the e-lectures and guest lectures. The mark is made of 10 equally weighted quizzes.
  • Problem sets: Two python problem sets. Each problem set is worth 10%.
  • Project: proposal: Short proposal for using data to address an open-ended research problem. Marks will be awarded on evidence that the student has developed a relevant research question with an appropriate data source and methodology. 
  • Project: visualising and presenting information:  This project will assess student’s ability to decompose a complex, open-ended problem, select and analyse relevant data and present the results visually and orally. Marks will be awarded on the content and clarity of the visualisations, the strength of the accompanying writing, and participation in a peer feedback exercise.
  • Project: collecting and analysing data: This project will assess student’s ability to use data to analyse a real-world public transport operations issue. The mark will include components for data collection, data processing/information retrieval, modelling and interpretation.
  • Project: spatial supplement: The spatial supplement will allow the student to revisit the analysis employing spatial data skills like mapping.  Marks will be awarded for the relevance, accuracy, scope and presentation of the analysis.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Introduction to public transport and informatics Lecture (2 hr) LO3 LO7 LO10
Getting started with Jupyter Workshop (1 hr) LO10
Week 02 Reading and manipulating data with Python Workshop (2 hr) LO10
The importance of data Practical (1 hr) LO10 LO12
Week 03 Data Collection (1) Workshop (2 hr) LO1 LO4 LO9
Statistical significance with public transport data sources Practical (1 hr) LO9 LO10 LO12
Week 04 Formulating a research question Workshop (2 hr) LO6 LO9
Data for decisionmaking Lecture (1 hr) LO3
Week 05 How to plot Workshop (2 hr) LO2 LO9
Introducing the project Lecture and tutorial (1 hr) LO6 LO9
Week 06 Data collection (2) Workshop (2 hr) LO1 LO8 LO10
Visualisation of big data sets Lecture (1 hr) LO2 LO10
Week 07 Presentations for the Visualisation component of the project Presentation (3 hr) LO1 LO2
Week 08 Models 1: All models are wrong but some models are useful Workshop (2 hr) LO11
Applications of data to transport operations Lecture (1 hr) LO3 LO4 LO10
Week 09 Models 2: Ordinary Least Squares Workshop (2 hr) LO11 LO12
Equity Practical (1 hr) LO5 LO6 LO8 LO9
Week 10 System Design Workshop (2 hr) LO6 LO8 LO11
The future of public transport Practical (1 hr) LO3 LO5 LO7
Week 11 Behavioural models Workshop (2 hr) LO9 LO11
Spatial considerations Lecture (1 hr) LO1 LO12
Week 12 Making maps Workshop (2 hr) LO2 LO10
Making maps in Python Workshop (1 hr) LO2 LO10
Week 13 Spatial analysis Workshop (2 hr) LO1 LO10 LO12
Beyond the classroom Workshop (1 hr) LO6 LO9

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. identify evidence of theoretical issues in the data and evaluate their significance
  • LO2. present data-focused analysis in visual and oral contexts
  • LO3. demonstrate understanding of the broader context for public transit including regulatory, equity, economic and environmental considerations
  • LO4. demonstrate knowledge of ethical issues and professional standards around the gathering and use of transport data
  • LO5. demonstrate an interdisciplinary evaluation of the public transit system including social, environmental and economic perspectives
  • LO6. decompose complex problems into tasks in a systematic way
  • LO7. employ public transport terminology fluently
  • LO8. perform calculations related to public transport planning and operations
  • LO9. develop solutions to open-ended public transit questions and support the solutions with evidence
  • LO10. apply data science tools to analyse public transport systems
  • LO11. select and apply appropriate modelling techniques
  • LO12. apply theoretical understanding of statistical methods to practical problems around data collection, statistical inference and interpretation.

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
In response to student feedback, the assessments have been restructured to place less emphasis on online quizzes and provide more scaffolding in the major project. Additionally, content on queuing theory is replaced with content on spatial analysis.

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.