BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//pyconde-pydata-2025//speaker//DCTDRA
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-pyconde-pydata-2025-RAHBEP@pretalx.com
DTSTART;TZID=CET:20250425T140000
DTEND;TZID=CET:20250425T143000
DESCRIPTION:Construction work in national railroad networks often disrupts 
 train traffic\, making it vital to estimate hourly train numbers for effec
 tive re-routing. Traditionally managed by humans\, this process has been a
 utomated due to staff shortages and demographic changes. DB Systel GmbH\, 
 Deutsche Bahn's IT provider\, leveraged machine learning and artificial in
 telligence to estimate train traffic during construction. Using Python and
  frameworks like Pandas\, scikit-learn\, NumPy\, PyTorch and Polars\, thei
 r solution demonstrated significant benefits in performance and efficiency
 .
DTSTAMP:20260421T224300Z
LOCATION:Zeiss Plenary (Spectrum)
SUMMARY:Forecast of Hourly Train Counts on Rail Routes Affected by Construc
 tion Work - Sebastian Folz\, Dr Maren Westermann
URL:https://pretalx.com/pyconde-pydata-2025/talk/RAHBEP/
END:VEVENT
END:VCALENDAR