One of the solutions could be a Bike Balancing Map that used a color-code system to represent balanced stations, stations that should receive bikes, and stations that should give away bikes.

This challenge uses Lisbon data to propose a data-driven solution for finding the missing segments in a scattered cycling network of a city.

Most teams managed to produce a map of Lisbon indicating the pavement quality in the locations where the dataset images were taken. A similar model could be applied to any city, which makes it very scalable.

What if we could increase the number of e-scooter journeys by up to 12%? Easy-to-find e-scooters in strategic locations can be the answer.