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Unit of study_

CIVL5701: Transport Networks

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

This unit of study aims to provide a comprehensive introduction to the mathematical representation, characterisation, analysis, and practical applications of transport networks. It covers the mathematical design of transport networks, the resolution of routing, matching, and other related combinatorial problems, and highlights key distinctions between public and private transport networks. Students will develop an understanding of algorithm efficiency, network economics, and concepts such as users' equilibrium and systems' optimum. The course employs a combination of lectures and tutorials. Students will design a public transport network using a representation of a real-life city in a semester-long group project. By the end of the course, students will know the fundamentals of networks theory and understand how to use it to model different transport systems.

Unit details and rules

Unit code CIVL5701
Academic unit Civil Engineering
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
None
Corequisites
? 
None
Assumed knowledge
? 

CIVL2700 OR CIVL9700

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Andres Fielbaum Schnitzler, andres.fielbaum@sydney.edu.au
Type Description Weight Due Length
Small continuous assessment Weekly/Fortnightly reports on project "Designing a Tramless Network for Sydney"
Partial guided reports of the advances in the semester-long project
15% Multiple weeks 3-4 pages each
Outcomes assessed: LO1
Small test First half exam
Written exam covering weeks 1-5
25% Week 06 2 hours
Outcomes assessed: LO2
Small test Second half exam
Written exam covering weeks 7-11
25% Week 12 2 hours
Outcomes assessed: LO3 LO4
Presentation group assignment Final presentation
Final presentation of the group project
10% Week 13 15 minutes
Outcomes assessed: LO1
Assignment group assignment Final report
Final report of the group project
10% Week 13
Due date: 03 Nov 2023 at 23:59
10 pages
Outcomes assessed: LO1
Presentation group assignment Individual Q&A session
Q&A session after the group presentation with individual marks
15% Week 13 15 minutes
Outcomes assessed: LO1
group assignment = group assignment ?

Assessment summary

  • First-half Exam, Week  6, 25% In class

  • Second-half Exam, Week 12, 25% In class

  • Project Design of public transport network: 50%

    • Weekly/quarterly partial reports 15%, 

    • Final presentation 10%, 

    • Final report 10%, 

    • Individual responses 15%.

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.

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 Motivation, examples, mathematical representation. Lecture (2 hr) LO1 LO2 LO4
Presentation of the course project Project (1 hr) LO1
Week 02 Traditional problems on a given transport network - Dijkstra's algorithm Lecture (2 hr) LO2
Lines structures: Types and proposal Project (1 hr) LO1
Week 03 Evaluation, the ‘illties’ Lecture (2 hr) LO2 LO3
Generalised costs Project (1 hr) LO1
Week 04 Linear and integer optimisation Lecture (2 hr) LO2
Route choice, vehicles' capacity, and frequencies. Project (1 hr) LO1
Week 05 Non-linear optimisation Lecture (2 hr) LO2
Mid-exam preparation Lecture and tutorial (1 hr) LO2
Week 07 Public transport: Strategic decisions Lecture (2 hr) LO1 LO3
Finding a demand-supply-equilibrium frequencies Project (1 hr) LO1
Week 08 Scale, scope economies, and network effects in public transport networks Lecture (2 hr) LO1 LO2 LO3
Mid-term project presentations Project (1 hr) LO1
Week 09 Supply-demand equilibrium in public transport Lecture (2 hr) LO1 LO3 LO4
Operational costs, total budget, and users costs Project (1 hr) LO1
Week 10 Private networks: Externalities, mode choice, and paradoxes Lecture (2 hr) LO3 LO4
Redesign of the network Project (1 hr) LO1
Week 11 Cyclepaths and bikesharing Lecture (2 hr) LO2 LO4
Second half exam preparation Lecture and tutorial (1 hr) LO3 LO4
Week 13 Final presentations of the course project Presentation (3 hr) LO1

Attendance and class requirements

Weekly in-person (online) attendance is expected as per University requirements. There will be in-class activities and presentations critical to learning.

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. develop a public transport network plan, share with class
  • LO2. use a transport network model: understand the representation of transport problems as combinatorial problems on networks; understand the algorithmic reasoning to solve problems on networks; understand other techniques to solve the problems
  • LO3. understand the main differences between public and private transport networks: draw implications of the externalities characterising each of them; practice the methods of network analysis.
  • LO4. analyse the implications of demand-responsive networks: compare global optimisation vs users’ equilibrium; practice different policies to align individual and societal objectives; discuss equity and implementability issues.

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

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

The course has been updated in many directions to include more aspects about mathematical analysis and optimisation on transport networks. We have also added a semester-wide applied project.

COMPLEMENTARY READINGS

 

  1. Santi, P., Resta, G., Szell, M., Sobolevsky, S., Strogatz, S. H., & Ratti, C. (2014). Quantifying the benefits of vehicle pooling with shareability networks. Proceedings of the National Academy of Sciences, 111(37), 13290-13294. (Week 1)

  2. Porta, S., Crucitti, P., & Latora, V. (2006). The network analysis of urban streets: A dual approach. Physica A: Statistical Mechanics and its Applications, 369(2), 853-866. (Week 1).

  3. Network Optimization: Continuous and Discrete Models, Dimitri Bertsekas, Chapter 2. (Week 2)

  4. Farahani, R. Z., Miandoabchi, E., Szeto, W. Y., & Rashidi, H. (2013). A review of urban transportation network design problems. European Journal of Operational Research, 229(2), 281-302. (Week 3)

  5. Wu, H., & Levinson, D. (2020). Unifying access. Transportation Research Part D: Transport and Environment, 83, 102355. (Week 3)

  6. Borndörfer, R., Grötschel, M., & Pfetsch, M. E. (2007). A column-generation approach to line planning in public transport. Transportation Science, 41(1), 123-132. (Week 4, week 7).

  7. Mauttone, A., Cancela, H., & Urquhart, M. E. (2021). Public Transportation. Network Design with Applications to Transportation and Logistics, 539-565. (Weeks 4, 5, 7)

  8. Mikishanina, E. (2023). Nonlinear Optimization Logistic Model in the Problem of Cargo Transportation. Transportation Research Procedia, 68, 133-137. (Week 6)

  9. Jara-Díaz, S., Fielbaum, A., & Gschwender, A. (2017). Optimal fleet size, frequencies and vehicle capacities considering peak and off-peak periods in public transport. Transportation Research Part A: Policy and Practice, 106, 65-74. (Weeks 6,9).

  10. Walker, J., (2012). Human transit: How clearer thinking about public transit can enrich our communities and our lives. Island Press. (Weeks 7-9).

  11. Levinson, D. (2022). Sydney FAST 2030: A Proposal for Faster Accessible Surface Transport (FAST).

  12. Durán-Micco, J., & Vansteenwegen, P. (2022). A survey on the transit network design and frequency setting problem. Public Transport, 14(1), 155-190. (Week 8)

  13. Fielbaum, A., Jara-Diaz, S., & Gschwender, A. (2020). Beyond the Mohring effect: Scale economies induced by transit lines structures design. Economics of Transportation, 22, 100163. (Week 9)

  14. Wu, H., & Levinson, D. (2021). Optimum stop spacing for accessibility. European Journal of Transport and Infrastructure Research, 21(2), 1-18.

  15. Hymel, K. M., Small, K. A., & Van Dender, K. (2010). Induced demand and rebound effects in road transport. Transportation Research Part B: Methodological, 44(10), 1220-1241. (Week 10)

  16. Mogridge, M. J. H., Holden, D. J., Bird, J., & Terzis, G. C. (1987). The Downs/Thomson paradox and the transportation planning process. International Journal of Transport Economics/Rivista internazionale di economia dei trasporti, 283-311. (Week 10)

  17. Roughgarden, T. (2007). Routing games. Algorithmic game theory, 18, 459-484. (Week 10)

  18. Basso, L. J., & Jara-Díaz, S. R. (2012). Integrating congestion pricing, transit subsidies and mode choice. Transportation Research Part A: Policy and Practice, 46(6), 890-900. (Week 10)

  19. Frade, I., & Ribeiro, A. (2015). Bike-sharing stations: A maximal covering location approach. Transportation Research Part A: Policy and Practice, 82, 216-227. (Week 11)

  20. Mauttone, A., Mercadante, G., Rabaza, M., & Toledo, F. (2017). Bicycle network design: model and solution algorithm. Transportation Research Procedia, 27, 969-976. (Week 11)

Disclaimer

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