Capture of POI for charging stations


Enabling the electrified future

Thinking out of the box. Using crowd data to define charging stations.

The widespread use of electric vehicles all over the world is still in its infancy, hence the involved parties are trying to cure its childhood illnesses.

One of the challenges in electric mobility is the electric vehicles’ (EV) limited travel distance and availability of charging infrastructure. From a customer point of view, the charging procedure needs to be integrated into everyday life rather than losing time taking detours.

Where’s the juice?

In the race for the distribution of this future technology, no one can afford to stay behind – especially, an automotive OEM that is well known for its premium cars’ acceleration and speed! Thus, it was crucial for the OEM to understand its customers’ mobility behaviour to provide charging points for EV’s where they are needed. Well known for its unorthodox solutions, umlaut was asked for help to create transparency through an innovative approach: umlaut’s crowd data!

Motivated by applying new approaches to assess the performance of mobile network services, umlaut developed a Software Development Kit (SDK) which gathers data from end user devices. The SDK is already integrated in more than 800 applications reaching more than 200 million devices worldwide and generating almost 5 billion data points per day. This data has been successfully used to explore many insights related to the network performance and quality of experience of mobile networks and services.

Digital does it.

It was time to ask new questions! The high penetration of smartphones and the resulting large-scale location information affords a considerable opportunity for commercial, governmental, and academic applications using analysis of people’s locations and movements. Using the OEM’s knowledge of its customer profiles, the solid crowd data base and a clear understanding of electric mobility and charge point requirements, umlaut got started!

Critical success factors were flexibility, scalability, and reliability in handling and processing the data. Thus, the usage of cloud services was indispensable. Special efforts were made to develop methodologies for cleaning the data, dealing with missing features, and deriving statistically valid insights on different metrics.

The power of the crowd.

By combining crowd data with the well-known customer profiles, umlaut was able to find the OEM’s potential customers within the sheer amount of data points. Having identified these users, the samples were aggregated and plotted on a map and frequently driven roads and regularly visited points of interest (POI), such as supermarkets, gym’s and fuel stations, were highlighted.

Having provided the resulting mobility behaviour of the identified users and the list of highly attractive POI, the OEM was now prepared to approach specific asset and infrastructure owners rather than guessing potential cooperation partners.

Thanks to a small piece of software and by thinking out of the box, umlaut was able to provide additional transparency and to add value by simultaneously saving time and manpower.