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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.

Code CIVL3704
Academic unit Civil Engineering
Credit points 6
Assumed knowledge:
MATH1005 AND CIVL2700. Understanding of statistical inference. Familiarity with the urban transport network and basic concepts in transport studies

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.