We have collected strictly anonymous and open access data, analyzed it using open source libraries in the Python language, and created a minimal demo application shown in the slides above. Visit our open source repository to see details in the README, give us your ★, leave us an 🗨️ Issue with your questions.
Imagine you are responsible in the city of Lucerne (or any other Swiss city) to plan for better pedestri-an infrastructure. For this you need to know how many people walk past specific locations during a day. If you knew which two hours of the day you would need to count to extrapolate the data for the whole day and if you had a tool you could enter this data to automatically get the daily volumes your job would be a lot easier. Maybe the tool would even adjust for the day of the week or weather.
The problem is, at the moment no such tool exists and we don’t really know which hours are best for doing counts to extrapolate to data to the whole day. Based on data of automatic pedestrian counters in Zurich and Basel, pedestrian flow charts and extrapolation factors could be derived with clever algorithms. This then could be translated into a practical tool for entering the data to easily get results. The challenge is submitted for pedestrian volumes but the same would apply for bicycle data. No such tool exists in Switzerland – neither for walking nor for cycling data.
Further information with the link to data sources here.
In addition to the analytical components, we have thought about the implications of this project in terms of open data publication, open source resources, and even to the possibility of an open hardware / solution to people counting. Visit our Slack channel for more background discussion.
By Wolf-Dieter - Own work, CC BY 2.5, Link