DataSim: A Machine Learning-powered simulation tool for rail timetable optimisation

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This project will deliver a Machine Learning powered simulation tool called DataSim, to empower rail operators with the ability to explore the impact of timetable changes on the network state. With DataSim, operators could simulate different scenarios and find the optimal solutions for efficient and reliable rail scheduling. DataSim uses a map interface which intuitively depicts rail asset position. Through this, rail operators can view the differences between planned and actual train positions, analyse the time lost or gained at each section of the journey, and view the arrivals and departures for each station on the network. This allows rail operators and decision makers to take a scientific approach to data analysis, mitigating management problems by empowering analysts to visualise and simulate services and the cascading impacts of unexpected delay scenarios including weather, trespass incidents, hardware failure, and other unforeseen circumstances.