Future Digital Rail:a framework forlong term location data curation forrailway asset management

Since the1800sknowing the location of (mapping) assets(in mile-chains, distance from the station)has been a fundamental part of railway operation/management. Recently, use of location-enabled digital data has escalated, driven by initiatives including the UK BIMmandate1, and data from satellite/remote sensing imagery, geographical information systems, airborne and terrestrial laser scanning, GPS-enabled devices and other sensors. Location-data benefits to railway operations are extensive-e.g. Network Rail uses aerial survey data to reduce the risk of working in the wrong place, with fewer people trackside for less time, and to minimise environmental damage2. However, managing(curating)this quantity of location-data is challenging:
To engender trust, data needs to be updated automatically (humans don't do updates).
Different foundational representations of location mean that assets have multiple representations (e.g. a signal box: a detailed 3D engineering drawing; a point on a 2Dmap;a pole in 3D virtual reality).
Data needs to be systematically documented (via metadata)
With some railway assets having a lifespan of 100+years, curation needs to be future proof.
Costs are incurred by storing data that will not be needed in future.

Currently, the operational focus of railway asset management strategies means they typically cover a 5-year period with only tangential consideration given to data(e.g. Network Rail3, Crossrail4). Long term location-data curation is not seen as an operational priority. In contrast, National Mapping Agencies(NMA)have extensive long term location-data curation expertise(the Ordnance Survey has curated map data for GB since 1783, digitally since 19715).Combining NMA expertise and railway asset management, this PhD will address: How should railway asset managers update and curate their location data now, in order to best support Future Digital Rail?