Browse courses to find something that interests you.
This is a free, self-paced online course.
This taster course explores how UTS and Sydney Trains are collaboratively working towards developing a timetable robustness evaluation model, using machine learning. This is the first data-driven model that can provide detailed, station-level, line-level and network-level analysis and evaluation results and predict in real-time, the delay effect after capturing delays.
The outcome of this innovative application of the intelligent timetable evaluation technology significantly reduces delay-caused losses and increases operation efficiency. This enables the train operating system to meet performance metrics and pursue timely recovery from incidents.
The model will be able to assess timetables and response plans to ensure that they are operationally robust and resilient. Based on the statistics, improving the on-time running rate by 1% could potentially save customer ‘lost-minute’ value by $5 million.
The proposed generic model can be easily applied to other traffic scenes, with subtle refinements. This work has demonstrated to train operating companies that they can produce highly detailed and granular information to develop targeted timetable design and real-time scheduling strategies. Importantly, for rail managers and controllers, end-to-end timetable evaluation and delay prediction is automatically achieved by data-driven techniques. This eliminates the need for domain expertise and hard-core feature extraction.
This course will provide an opportunity for participants to engage with the following learning outcomes:
This taster course is suitable for anyone who is interested in machine learning and a real-life application of its benefits, as demonstrated through a case study of the Sydney train network.
Learn the power and language of data analysis and get hands-on with key techniques for mining and analysing data. [10 wks, avg 5 hrs/wk]
Go in-depth into key machine learning concepts through theory, maths and programming. [10 wks, avg 5 hrs/wk]
Build your foundational data background to develop a skillset to run data mining and analysis projects. [10 wks, avg 5 hrs/wk]
Explore tools to analyse and present data on NSW communities during the early to mid-stages of COVID-19.
Understand the global response to COVID-19 through a data-driven approach, to explain how nations have responded.
Explore a case study investigating the impacts of COVID-19 and Australian bushfires, on our health and wellbeing.