A “flow-less” public transport experience
It’s time to address accessibility issues in society. Especially in times like ours where social distancing is important, for high risk groups such as elderly it could be very useful to be able to take the bus when it is least crowded. This challenge aims to help these groups achieve their “flow-less” experience in their day to day life.
You will be working with Sweden’s public transport data open API, extracting the data using the API and making predictions on when it is best to take public transport.
The main focus for the team will be to calculate/predict passenger flows on public transport from one stop to another. Can we optimize public transport use to minimise crowds? Can we give the right insights to high-risk groups?
This team is looking for 2-3 data scientists and 1-2 (Python) developers.