C3Subtitles: rC3 NOWHERE - Channels: Optimising public transport: A data-driven bike-sharing study in Marburg
back

Optimising public transport: A data-driven bike-sharing study in Marburg

If you suspend your transcription on amara.org, please add a timestamp below to indicate how far you progressed! This will help others to resume your work!

Please do not press “publish” on amara.org to save your progress, use “save draft” instead. Only press “publish” when you're done with quality control.

Video duration
00:41:54
Language
English
Abstract
Imagine you are running late for your bus and decide to grab a bike-sharing bike to get there in time. More often than not I found myself standing at an empty station only to miss my bus. Here, I present you my data-driven approach to avoid walking to empty bike-sharing stations.

I started collecting Nextbike data in Marburg many months back in order to solve my personal issue of facing empty Nextbike stations in Marburg. After collecting more than 1,000,000 data points, I turned towards the analysis to figure out which stations in Marburg to avoid when desperately needing a bike.

After finding the data-driven solution to that question, I expanded my study to not only answer questions for Nextbike users but also from the perspective of the city council to make the lives of all of us easier, healthier and eco-friendlier. After those statistical statements, I conclude my study with a more precise machine-learning based prediction of parked bikes to motivate data-driven optimisations in public transport.

Talk ID
rc3-nowhere-279
Event:
rc3-2021
Day
3
Room
Chaos-West TV
Start
8 p.m.
Duration
00:40:00
Track
Auf in die Zukunft!
Type of
Talk
Speaker
Martin Lellep
Talk Slug & media link
rc3-2021-cwtv-279-optimising-public-transport-a-data-driven-bike-sharing-study-in-marburg

Talk & Speaker speed statistics

Very rough underestimation:
159.1 wpm
872.0 spm
100.0% Checking done100.0%
0.0% Syncing done0.0%
0.0% Transcribing done0.0%
0.0% Nothing done yet0.0%
  

Talk & Speaker speed statistics with word clouds

Whole talk:
159.1 wpm
872.0 spm