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DTSTART:20250330T030000
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SUMMARY:Forecast of Hourly Train Counts on Rail Routes Affected by Constru
 ction Work - Sebastian Folz\, Dr Maren Westermann
DTSTART;TZID=Europe/Berlin:20250425T140000
DTEND;TZID=Europe/Berlin:20250425T143000
DTSTAMP:20260816T200242Z
UID:pretalx-pyconde-pydata-2025-RAHBEP@pretalx.com
DESCRIPTION:Construction work in national railroad networks often disrupts
  train traffic\, making it vital to estimate hourly train numbers for effe
 ctive re-routing. Traditionally managed by humans\, this process has been 
 automated due to staff shortages and demographic changes. DB Systel GmbH\,
  Deutsche Bahn's IT provider\, leveraged machine learning and artificial i
 ntelligence to estimate train traffic during construction. Using Python an
 d frameworks like Pandas\, scikit-learn\, NumPy\, PyTorch and Polars\, the
 ir solution demonstrated significant benefits in performance and efficienc
 y.
LOCATION:Zeiss Plenary (Spectrum)
URL:https://pretalx.com/pyconde-pydata-2025/talk/RAHBEP/
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