In the last twenty years the availability of vast amounts of data has enabled industries to gain insight into numerous aspects of their operation whose trends were previously unknown. The result is an unprecedented ability to predict operational needs, to evaluate performance of individuals or assets and prepare such industries for uncertainties. The rail industry currently produces large amounts of data that are, in many cases, not used to their full potential. The first case study demonstrates a novel method to identify and cluster distinct driver styles in use on a DC rail network. Using the optimal driver styles identified, improved ‘driver cultures’ were designed that are shown to provide up to 10% energy savings without the need for expensive in cab driver advisory systems. The second case study details data taken from a full fleet that were used to develop a statistical method to identify the minimum amount of vehicles that required energy metering whilst still providing an accurate mean energy consumption estimate. The identification of this minimum amount was then used to validate the fleet size intended for partial fleet metering options for UK rail networks.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:715606 |
| Date | January 2017 |
| Creators | Ellis, Robert Joseph |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/7461/ |
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