Meetup 27/10

Notes - collaboratively taken by the attendees - from our meetup on 13/10.

Short description Bike Data Project

Little is known about how cyclists move around in cities today. If there’s data available, it’s closed and restricted data sold by one single app provider or static data collected through manual counts. If we want to have more people cycling in cities and make the bicycle as easy and logical to opt for as the car, we also need to get the same data insights into cyclists’ behavior as we do have about car transport.

To make the Bike Data Project work we ask for a simple favor from the cycling community. We ask different types of cyclists (e.g. commuters, delivery riders, sportsmen and tourists) to track their bicycle rides via their preferred mobile application and donate their data to our community-driven bike data platform. Some people already track their activity for training purposes. That’s great but we also really want to take into account the short routes like commuting to work or just taking a quick ride to the grocery store. In return, the anonymous aggregated cycling data will be opened up to the public and can be freely used by anyone. The collective data gives us patterns we can use to demonstrate our cause.

Slack invite

With this link you can also join our Bike Data Project Slack: https://join.slack.com/t/bikedataproject/shared_invite/zt-hr00amgw-elYn9WbdFHLta8qQKW_wvQ

Agenda for the first Bike Data Project meetup (CET - Brussels time):

18:30 Introductions - tour de table 18:50 Project updates Bike Data Project 19:00 Q&A 19:15 Brainstorming sessions in different teams

  • Use cases of the open cycling data

  • Project & Data Dive event promotion

19:35 Closure and final remarks

Project Presentation​

Meetup #2: Bike Data Project in a nutshell

Group discussion

  • What other data would be useful to collect besides the GPS traces?

    • We could ask cyclists for additional information (e.g. what kind of bicycle they ride) and add that as metadata.

    • Instead of asking cyclists, we could also see if AI can help us identify the kind of bicycle ride.

  • Learning from other related project: it's very important to make it clear why cyclists should participate.

    • Incentives like personal feedback or other benefits can encourage cyclists to share their data.

  • It's a great idea to match factual information (GPS traces) with subjective information like the perception on road safety.

  • We all agree: the ultimate goal of the Bike Data Project is to change cities and make them more bike-friendly with open data.

  • Unanswered question: what location data are collected on iOS? Does Google Maps also automatically track users on iOS?

Other cycling data projects - shared by participants