Flare’s expanding capabilities enable data scientists to collect and analyse data from e-mobility usage and other forms of personal transport. Flare’s intelligent incident detection architecture provides a wealth of insights for the customer, all of which come together to enhance the safety of the end user and provide a competitive edge to the customer with knowledge of risk to the end user. There are several reasons why Flare is a great opportunity for data scientists to better understand the unique challenges of your business:

1. Intelligent Incident Detection: Since 2016, Flare’s machine learning-based approach to personal safety has been trusted to keep vulnerable road users safe by promptly alerting close contacts and nearby users to the location of the incident. The data is logged to the Flare dataset, on which our XGBoost-based model architecture is retrained to further enhance accuracy and recall.
2. Comprehensive Data Collection: Flare collects and analyses motion sensor data from the end user’s phone. Over time, this has produced an extensive and expanding dataset from which a wealth of knowledge can be inferred about serious incidents and users’ habits in using personal transport, providing a powerful analytics capability.
3. Geospatial Insights: Flare’s collection of anonymised geolocation data enables data scientists to observe when and where accidents happen, in real time. This provides data scientists with feedback to make rapid adjustments to strategy, providing an invaluable, granular picture of the risks to the end user associated with specific locations and at specific times.

Overall, Flare is a powerful tool that can help data scientists gain a better understanding of their end users by providing intelligent incident detection, comprehensive data collection, and geospatial insights. By using Flare, data scientists can make more informed decisions that improve user satisfaction, ultimately leading to increased revenue and growth for their business.

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