Note: This unit version is currently under review and is subject to change!

CIVL3704: Transport Informatics (2019 - Semester 1)

Download UoS Outline

Unit: CIVL3704: Transport Informatics (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Senior
Faculty/School: School of Civil Engineering
Unit Coordinator/s: Dr Cafe, Peter
Levinson, David
Moylan, Emily
Emmeline, Yeo
Session options: Semester 1
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Prohibitions: ENGG2851.
Brief Handbook Description: 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.
Assumed Knowledge: MATH1005 AND CIVL2700. Understanding of statistical inference. Familiarity with the urban transport network and basic concepts in transport studies.
Lecturer/s: Moylan, Emily
Timetable: CIVL3704 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 1.00 1 13
2 Workshop 2.00 1 13
3 Tutorial 2.00 1 13
4 E-Learning 1.00 1 13
5 Independent Study 4.00 1 13

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(6) Communication and Inquiry/ Research (Level 3)
1. Identify evidence of theoretical issues in the data and evaluate their significance.
2. Present data-focused analysis in visual and oral contexts.
(8) Professional Effectiveness and Ethical Conduct (Level 1)
3. Demonstrate understanding of the broader context for public transit including regulatory, equity, economic and environmental considerations.
4. Demonstrate knowledge of ethical issues and professional standards around the gathering and use of transport data.
(5) Interdisciplinary, Inclusiveness, Influence (Level 3)
5. Demonstrate an interdisciplinary evaluation of the public transit system including social, environmental and economic perspectives.
(4) Design (Level 2)
6. Decompose complex problems into tasks in a systematic way.
(2) Engineering/ IT Specialisation (Level 2)
7. Employ public transport terminology fluently.
8. Perform calculations related to public transport planning and operations.
(3) Problem Solving and Inventiveness (Level 3)
9. Develop solutions to open-ended public transit questions and support the solutions with evidence.
(1) Maths/ Science Methods and Tools (Level 3)
10. Apply data science tools to analyse public transport systems
11. Select and apply appropriate modelling techniques. Apply theoretical understanding of statistical methods to practical problems around data collection, statistical inference and interpretation.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Reading quizzes No 15.00 Multiple Weeks 2, 3, 4, 5, 7, 10,
2 Comprehension quizzes No 15.00 Multiple Weeks 2, 3, 4, 7, 8, 10,
3 Problem Sets No 30.00 Multiple Weeks 1, 2, 6, 8, 10, 11,
4 Project: visualising and presenting information No 15.00 Week 8 1, 2, 6, 8, 9, 10, 11,
5 Project: Collecting and analysing data No 25.00 Week 13 1, 2, 3, 4, 5, 9, 10, 11,
Assessment Description: Short online quizzes accompany the provided readings. The mark is made of 10 quizzes worth 1.5pts each. The quizzes must be completed by 23:59pm on the Sunday of the week they are assigned.

Short online quizzes accompany the e-lectures and guest lectures. The mark is made of 10 quizzes worth 1.5pts each. The quizzes must be completed by 23:59pm on the Sunday of the week they are assigned.

Three python problem sets will be due in Week 3, Week 6 and Week 9. Each problem set is worth 10 pts. The problem sets must be submitted online by 23:59pm on the due date.

A project on visualising and presenting information will assess the 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, a short oral presentation, and participation in a peer feedback exercise. Oral presentations and peer feedback will occur in Workshop in Week 7. The report is due online in Week 8.

A project on collecting and analysing data will assess the 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. The report is due online in Week 13.

A lateness penalty of 5% will be deducted for each day after the due date.
Assessment Feedback: Online quizzes will provide immediate feedback. Feedback on other assessments will be returned within 2 weeks of the due date.
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 8 Assessment Due: Project: visualising and presenting information
Week 13 Assessment Due: Project: Collecting and analysing data

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Civil/ Project Management 2019, 2020
Civil 2016, 2017, 2018, 2019, 2020
Civil / Science 2019, 2020
Civil/Science (Health) 2019, 2020
Civil Mid-Year 2016, 2017, 2018, 2019, 2020
Civil/Science (Medical Science Stream) 2019, 2020

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 3) No 22.21%
(7) Project and Team Skills (Level 1) No 0%
(8) Professional Effectiveness and Ethical Conduct (Level 1) No 12.5%
(5) Interdisciplinary, Inclusiveness, Influence (Level 3) No 4%
(4) Design (Level 2) No 6.48%
(2) Engineering/ IT Specialisation (Level 2) No 13.23%
(3) Problem Solving and Inventiveness (Level 3) No 7%
(1) Maths/ Science Methods and Tools (Level 3) No 34.46%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.