Graz.
smart accident prevention.
City Partner
Graz, Austria
Winning Start-up/SME
Solution statement
Platform designed to improve road safety and the work of all Graz Linien drivers on real-time accident reporting.
Challenge Title
How can data collected in the course of a traffic accident help and be meaningfully enriched in order to derive patterns for accident prevention?
Programme Edition
RAPTOR 2024
Challenge
How can data collected in the course of a traffic accident help and be meaningfully enriched in order to derive patterns for accident prevention?
CURRENT SITUATION
Every incident in the operation of the Graz Lines, such as a grazing of another vehicle or a passenger fall, must be documented by the drivers in the form of an analog accident form.
Currently, the data is not digitally processed or analysed, which means that no accident hotspots can be derived.
Expected to-be situation
- Tablet equipment: In the future, all Graz Linien drivers will be equipped with tablets, which will enable the digital recording of accident data.
- Overcoming the language barrier: The software solution should specifically address the needs of employees with a migration background by offering intuitive interfaces and multilingualism. Innovative solutions, including AI-supported translation services, can be considered here.
- Ease of use for drivers: The user interface of the application should be designed in such a way that it is easy to understand and use for all drivers, regardless of their technical background. User-friendliness is crucial for broad acceptance.
- Insurance processing and integration into urban systems: The digitalized accident report should not only take into account the needs of drivers, but also the requirements of insurance processing and integration into city systems. Interfaces to existing IT infrastructures are of the utmost importance here.
Accidents can be measurably reduced. High data quality increases the efficiency of accident handling.
KPIs:
- Automatic collection of accident data
- Recognized patterns (meaningful correlations are recognized)
- Integration of external sources (weather, sensors, movement data)
- Early warning system possible (constellations can be recognised and drivers can be warned/sensitised)
