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SUMMARY:Data-driven bicycle driving cycles via mixed-integer programming -
  Malte Rothhämel
DTSTART;TZID=Europe/Amsterdam:20260916T111400
DTEND;TZID=Europe/Amsterdam:20260916T114400
DTSTAMP:20260918T121422Z
UID:pretalx-crbam-2026-WW9TGC@pretalx.com
DESCRIPTION:The study proposes a novel approach based on Mixed-Integer Pro
 gramming (MIP) to\nderive a representative driving cycle for bicycles usin
 g statistical data. The model\nprovides a flexible tool for generating dri
 ving cycles based on relatively simple data-sets.\nIt can be used both as 
 a way of setting performance standards for bicycles and related\ninfrastru
 cture\, and as a validation benchmark in the development of other methods.
 \nThe approach leverages the strength of Mixed Integer Programming to gene
 rate driving\ncycles which adhere closely to the original dataset on key f
 eatures which can be selected\nby the user.\nTo introduce the model and th
 e MIP framework\, we provide a case study based on data\nfrom the Mon Reso
  Vélo mobile application from the city of Montréal. A driving cycle\nis 
 optimized to fit the data set\, based on available features such as veloci
 ty distribution\,\nstops in-between\, and acceleration profiles\, as well 
 as the requirement of the driving\ncycle being continuous.\nThe method can
  easily be applied to other datasets containing similar meta-data\, as\nwe
 ll as be extended in order to generate a highly specific driving cycle tai
 lored to cities\nwith unique infrastructure or elevation profiles. Overall
 \, this study makes a significant\ncontribution towards quantifying and op
 timizing bicycle energy efficiency\, advancing\nsustainable urban mobility
 .
LOCATION:Plenary room (Lecture Room 2.2)
URL:https://pretalx.com/crbam-2026/talk/WW9TGC/
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