Outline

Overview

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.

Unit details and rules

Unit code	CIVL3704
Academic unit	Civil Engineering
Credit points	6
Prohibitions ?	None
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

Coordinator	Emily Moylan, emily.moylan@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Reading quizzes Online quizzes	5%	-	n/a
Outcomes assessed:
Assignment	Comprehension quizzes Online quizzes	5%	-	n/a
Outcomes assessed:
Assignment	Problem sets Problem sets on Ed due Week 3, 5	20%	-	n/a
Outcomes assessed:
Assignment	Project: Proposal Short proposal for the data collection project	5%	Week 07	n/a
Outcomes assessed:
Assignment	Project: visualising and presenting information Submission of visualisation plus peer feedback	15%	Week 08	n/a
Outcomes assessed:
Assignment	Project: Spatial Supplement Creation of maps and analysis	15%	Week 10	n/a
Outcomes assessed:
Assignment	Project: collecting and analysing data Report on data collection, analysis and interpretation	35%	Week 13	n/a
Outcomes assessed:

Assessment summary

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: 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: 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: spatial visualisation: The spatial visualisation will allow the student to revisit their visualisation skills while employing spatial data. The maps should reflect the topic presented in the Project Proposal. Marks will be awarded for the relevance, accuracy, scope and presentation of the analysis.
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. It should build on material prepared for the Visualisation, Project Proposal and Spatial Visualisation assessments.

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.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to public transport and informatics	Lecture (1 hr)
Week 01	Getting started with Jupyter	Workshop (2 hr)
Week 02	The importance of data	Practical (1 hr)
Week 02	Reading and manipulating data with Python	Workshop (2 hr)
Week 03	Data for decision making	Seminar (1 hr)
Week 03	Data Collection (1)	Workshop (2 hr)
Week 04	Transport Informatics Project	Lecture (1 hr)
Week 04	Formulating a research question	Workshop (2 hr)
Week 05	Principles of Visualisation	Seminar (1 hr)
Week 05	How to plot	Workshop (2 hr)
Week 06	Data collection (2)	Workshop (1 hr)
Week 06	Data collection (2)	Workshop (2 hr)
Week 07	Spatial data and visualisations	Lecture (1 hr)
Week 07	Making Maps	Workshop (2 hr)
Week 08	Making Maps (2) Python	Workshop (1 hr)
Week 08	Making Maps (3) GIS Software	Workshop (2 hr)
Week 09	Applications of data and models to transport operations	Seminar (1 hr)
Week 09	Models 1: All models are wrong but some models are useful	Workshop (2 hr)
Week 10	Models 2: Ordinary Least Squares	Workshop (1 hr)
Week 10	Spatial analysis	Workshop (2 hr)
Week 11	Equity	Practical (1 hr)
Week 11	System Design for social equity	Workshop (2 hr)
Week 12	Life Cycle impacts of transit operations	Lecture (1 hr)
Week 12	Environmental Sustainability Calculations	Practical (2 hr)
Week 13	Beyond the classroom	Workshop (1 hr)
Week 13	Project Workshop	Workshop (2 hr)

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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

In response to student feedback, the spatial component of the class is moved forward to Week 8.

Additional information

Ed, the online platform that we use for coding.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

CIVL3704: Transport Informatics

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect