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Predictive models for rail defects for ProRail
Quote :
"Xomnia has helped us very well with setting up our DataLab, by providing data science talents (traineeship proposition) and infrastructure expertise. Paul van der Voort, Program manager DataLab"More about the case:
ProRail is the infrastructure manager of the Dutch Railways. Xomnia has helped us in developing predictive models for two kinds of rail infra disturbances: rail defects and trespassers (people along the track). The trespasser dashboards predict the days and locations with an increased risk of people along the track (unallowed), based on numerous data sources like locations of schools, holidays, weather etc. With this dashboard, we can focus our surveillance capacity more effectively. The predictive models for rail defects identify spots in the railway network with a high risk of cracks in the rail and subsidence of the track, based on several data sources of track condition and influencers. Next to this, Xomnia has advised ProRail in the setup of our Big data platform.
Category:
Data Science traineeship Machine learning & Predictive modelling Traineeships Transportation/Trucking/RailroadDate:
15/03/2017
