Can AI make our emergency services more efficient?

Data as a Starting Point

The group of students received a dataset to work with, containing information about interventions by emergency services within the city. Previous student teams have already extracted valuable insights from this data, and with various artificial intelligence (AI) applications, they sought answers. Aleksander and his groupmates also tackled this, asking: how can we use this data to provide advice for better deployment and optimization of response times for these emergency services.

Cluster Mixture Model

The dataset contained information about incidents within the city. The students decided to cluster these using a so-called cluster mixture model. With this AI model, the city map can be visualized based on historical data with clusters of accidents. These clusters provide better insight into the focus areas for emergency services and are thus useful in more effectively distributing vehicle placements, consequently accelerating response times. The clusters also teach us about the distribution of incidents and help identify hotspots. This information enables better decision-making.

In the video, Aleksander explains the project's scope and the challenges faced:

Collaboration

The Safety & Security Campus commissioned this project and collaborated with SPARC (Sharing Platform For Applied Research Co-Operation) and first responder organizations. The project was carried out within the AI for Society minor at the Fontys ICT InnovationLab on Strijp TQ. The minor is open to all students, even those without a technical background, who want to delve into AI.