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AACE-CED-570: Railway Digital Twin: Getting Best Value from Data

  • Full or part time
  • Application Deadline
    Thursday, August 29, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The railway industry is committed to transforming the way it works by fully deriving value from data. The advent of modern toolsets, libraries of machine learning algorithms and affordable Big Data platforms, presents a unique opportunity. Vast collections of railway data can now be viewed as a key asset to the industry. Insights about the behaviour and performance of the infrastructure will be systematically and scientifically derived through better analysis of this data.

The vision is to develop a “Digital Twin” of the railway network that captures the key factors that cause network disruption. The Digital Twin will be used to explore scenarios that include changes in operations, vehicle and infrastructure reliability, crew availability, planned interventions (maintenance, refurbishment and renewal) and external factors – including weather and climate change. The digital twin needs to continuously learn and update from multiple sources to provide the most current representation of network disruption.

The project would start with a comprehensive review of the data sources that are available both internal and external to the rail industry. The baseline data would then be the subject of systematic datamining. Once in an appropriate form, the application of GPU based deep learning and advanced analytics to classify and provide estimated parameters for possible disruptions would be undertaken.
Ultimately the desired output is a predictive model that is sufficiently granular to enable analysis of a specific area of interest over some specified period. The expected “accuracy” of the model – whether defined formally through data uncertainty or model confidence – is necessary to help constrain how the model is used to support business decisions.

An industry wide information architecture and information governance structure will coordinate information needs and establish a means of supplying these. Information will be shared across the industry and more widely enabling the development of new services and applications to the benefit of the railway and its customers.

This project will be undertaken in conjunction with Network Rail, who will provide for an extremely attractive and fully competitive tax-free stipend.

Candidates for this exciting role would:

- Demonstrate an enquiring mind with a relentless drive to seek new insights from data
- Demonstrate knowledge of statistics and computational techniques
- Want to develop skills in data science
- Want to develop skills in information technology to effectively exploit big data
- Want to contribute to the Digital Railway vision.

If you wish to discuss any details of the project informally, please contact Prof Andy Keane, CED research group, Email: , Tel: +44 (0) 2380 59 2944.

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

Research output data provided by the Research Excellence Framework (REF)

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