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Institute of Rescue Engineering and Civil Protection
The Goal The Problem
Picture: David Liuzzo; https://de.wikipedia.org/wiki/Datei:Karte_Deutschland.svg; Editing: Transparency
emergency response in Germany is largely based on
volunteers
Social and demographic changes are leading to a
reduced availability of volunteers
emergency services must be on scene within a few
minutes in order to provide an effective response
Increasingly dense traffic in metropolitan areas
causes emergency vehicles to move slow
!
The Solution
Each incident response generates data Data is stored in control centers
Lessons can be learned for future
incidents from these data
Using Existing Data to Support Operational Emergency Response in Germany Lennart Landsberg, David Ganske, Christopher Munschauer, Ompe Aimé Mudimu
Institute of Rescue Engineering and Civil Protection
Existing Data that can be Combined
Incident Level System Level Environmental Level
At time of Dispatch
- Time of day
- Day of week etc.
- Initial incident type
- Presumed threat level
- GPS positions
- Photo/video of incident
During incident:
- Updated incident type
- Actual threat level
- Unit Status
- Unit location
- Vehicle status information
- Consumables remaining
- Hospital occupancy level
- Historic incident data
- Availability of volunteers
- Staffing level
- Road network
- Traffic volume
- Major public events
- Demographics
- Topography
- Current weather
- Forecasted weather
and AI can Learn from
Using Existing Data to Support Operational Emergency Response in Germany Lennart Landsberg, David Ganske, Christopher Munschauer, Ompe Aimé Mudimu
Using Data to Show
Resilience of the
Fire Service
Resilience Level
Fire Station
Fire Station (not available*)
Major Incident
Result:
Developments and trends of the past years have shown
the need for solutions in order to be able to maintain the
current level response effectiveness.
One solution is an optimized use of available resources
which is only possible through an advanced development
in data analyzing and processing.
The increasing digitalization in addition makes the
collected data more and more useful for direct electronic
processing.
A closer look at the data basis reveals the
possibilities of continuous data evaluation by AI.
Dispatchers can be given the capability to identify
requirements minutes, hours or even days in
advance:
* deployed to an incident
Institute of Rescue Engineering and Civil Protection
Lennart Landsberg, M.Sc.
Mail: lennart_walter.landsberg@th-koeln.de
Phone: +49 221-8275-2109
Link to Research Paper:
http://idl.iscram.org/files/lennartlandsberg/2020/2240_LennartLandsberg_etal2020.pdf
Using Existing Data to Support Operational Emergency Response in Germany Lennart Landsberg, David Ganske, Christopher Munschauer, Ompe Aimé Mudimu

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EENA 2021 - Research corner (3/4)

  • 1. Institute of Rescue Engineering and Civil Protection The Goal The Problem Picture: David Liuzzo; https://de.wikipedia.org/wiki/Datei:Karte_Deutschland.svg; Editing: Transparency emergency response in Germany is largely based on volunteers Social and demographic changes are leading to a reduced availability of volunteers emergency services must be on scene within a few minutes in order to provide an effective response Increasingly dense traffic in metropolitan areas causes emergency vehicles to move slow ! The Solution Each incident response generates data Data is stored in control centers Lessons can be learned for future incidents from these data Using Existing Data to Support Operational Emergency Response in Germany Lennart Landsberg, David Ganske, Christopher Munschauer, Ompe Aimé Mudimu
  • 2. Institute of Rescue Engineering and Civil Protection Existing Data that can be Combined Incident Level System Level Environmental Level At time of Dispatch - Time of day - Day of week etc. - Initial incident type - Presumed threat level - GPS positions - Photo/video of incident During incident: - Updated incident type - Actual threat level - Unit Status - Unit location - Vehicle status information - Consumables remaining - Hospital occupancy level - Historic incident data - Availability of volunteers - Staffing level - Road network - Traffic volume - Major public events - Demographics - Topography - Current weather - Forecasted weather and AI can Learn from Using Existing Data to Support Operational Emergency Response in Germany Lennart Landsberg, David Ganske, Christopher Munschauer, Ompe Aimé Mudimu Using Data to Show Resilience of the Fire Service Resilience Level Fire Station Fire Station (not available*) Major Incident Result: Developments and trends of the past years have shown the need for solutions in order to be able to maintain the current level response effectiveness. One solution is an optimized use of available resources which is only possible through an advanced development in data analyzing and processing. The increasing digitalization in addition makes the collected data more and more useful for direct electronic processing. A closer look at the data basis reveals the possibilities of continuous data evaluation by AI. Dispatchers can be given the capability to identify requirements minutes, hours or even days in advance: * deployed to an incident
  • 3. Institute of Rescue Engineering and Civil Protection Lennart Landsberg, M.Sc. Mail: lennart_walter.landsberg@th-koeln.de Phone: +49 221-8275-2109 Link to Research Paper: http://idl.iscram.org/files/lennartlandsberg/2020/2240_LennartLandsberg_etal2020.pdf Using Existing Data to Support Operational Emergency Response in Germany Lennart Landsberg, David Ganske, Christopher Munschauer, Ompe Aimé Mudimu