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Predicting and automatically recognizing unusual crowd behavior with artificial intelligence

With more than two thirds of people in industrialized countries using smartphones that can be located, an enormous amount of data documenting user movement is being generated. Within the limits of respective data protection rules, those valuable data can be used to improve public safety and security in big cities and support urban planning.

Using artificial intelligence methods, analyzes pedestrian streams in real time as well as possible anomalies, and also predicts the latter to a certain extent. The private sphere of individuals remains protected as only processes mass data which makes individual identification impossible. offers users exact insights into crowd movement behaviors, issuing automated warnings of unusual or critical situations, including just-in-time predictions of crowd movement developments. draws on the following technologies: SIS Crowd Sensing, SIS Analysis

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This area is seeing unusually high amounts of people.
This area is at risk of being overcrowded in 15 minutes.

Probability: 75%
This area is at risk of being overcrowded in 15 minutes.

Probability: 75%
Additional time to react: 15 minutes
One of’ basic functions is the visualization of pedestrian streams in real time, providing continuous relevant live information.’ integrated artificial intelligence is able to generate automated warnings in case of anomalies in mass movement behavior.
Moreover, the technology can make short-term predictions about the development of pedestrian streams.
Such information can be of invaluable use to security forces, for example in order to avoid the overcrowding of public spaces.
When applied continuously, can also be used to analyze urban infrastructure, for example to support urban planning.
Mobile phone providers, too, can benefit from using by dynamically scaling their coverage based on the technology’s output.
Both SIS Crowd Sensing data and so-called mass location data sources (such as mobile phone providers) can serve as data sources for
Individual data protection is always guaranteed as only uses anonymous data.
Its significant scalability makes implementable beyond more limited environments such as individual neighborhoods; if necessary, country-wide interventions are possible.