Data Science Challenge

Eric Peukert, Universität Leipzig 

 Data Science Challenge 
  Tue, 7th  
☷  13:30 - 15:00  
  APB E007  

Get on your bike and go!
A good bicycle infrastructure forms the basis for sustainable, environmentally friendly, and resource-saving mobility in urban areas. However, the traffic transformation is only progressing in very small steps. With our Data Science Challenge, we want to open up new perspectives and fields of action with the analysis of digital bicycle data.

Surely cyclists who are on the road every day know exactly the problem areas of cycling in their own city: How to avoid large, unsafe streets? Where does the bike lane suddenly end in a pothole? How to get from A to B with as much green view and as little bicycle and car traffic as possible? While this is the knowledge that each individual bicyclist carries within themselves, analyzing publicly available urban bicycle traffic data and other urban geographical data can help us connect individual perspectives to analyze the big picture of urban bicycle infrastructure.

Four teams have participated in the Data Science Challenge and will present their results that will be evaluated by a jury of experts from research, cities and industry. 


Award ceremony

The Award Session will be held during the Dresden City Hall Reception on Wednesday night. First to third place will be rewarded with prize money from a pool of 1000 euros. 

Cooperation partners

The Data Science Challenge 2023 is carried out by ScaDS.AI Dresden/Leipzig in cooperation with BTW 2023 and the CUT (Connected Urban Twins) project of the City of Leipzig.

It is kindly supported by ↗ InfAI Leipzig.

The task for applicants was:

Choose a metropolitan area or a city with a sufficient density of sensors and other publicly available geodata for your analysis. In the linked document you can find exemplary data sources from Berlin, Munich, Hamburg, Dresden or Leipzig. Make sure that only trustworthy sensors are used, which do not provide false readings and have only a few dropouts in the recordings. If necessary, blend the data from different sources and clean them to ensure data quality.

Find interesting facts and patterns in the data sources and create an analysis that answers a question of social relevance. Possible initial approaches might include:

The result of the analysis can optionally be visualized or presented in purely textual form, for example as a recommendation for action. You decide which technologies, cloud services, and cloud technologies are used. The approach can integrate available services and tools or develop new ones.

Criteria for prototype/concept evaluation