Safer Roads through Analytics – Zendrive

zendrive analyticsMore than 90% of automobile accidents result from human error. 30% from phone use alone. These are staggering numbers and it’s no wonder personal and corporate insurance premiums continue to rise.

A company called Zendrive hopes to improve driving for everyone through better data and analytics. Zendrive takes a unique approach to data capture by using the sensors on a smartphone to measure anyone’s driving behaviour. When captured, they apply machine learning algorithms to turn that data from the phone’s sensors into actionable insights about driver safety.

Analytics of signals

Modern smartphones are packed with sensors that measure everything from location to acceleration to weather. Zendrive takes advantage of the phones’ core facilities by reading signals like usage, speed, swerves, hard stops, fast accelerations, fatigue, as well as weather, trip duration and time of day. These insights can be used real-time to track fleet-wide, or with an individual drivers’ focus, caution, and control on the road. All points to help improve driving abilities where needed.

From old to new

Every on demand enterprise using drivers for rideshare, carshare, and for delivering everything from meals to massages, is running into the same scaling hassle. As they get bigger, they have to deal with building tools and analytics to manage a young fleet. Zendrive’s use of on-phone data measurement and analytics offers a simple approach to deployment over old model hardware and custom built solutions.

Down the data road

It’s relatively simple to surmise where Zendrive could go with their technology.  One option that they are surely pondering would involve predictive analytics. For example, imagine you could include geographically archived data showing where accidents are most likely to happen married together with a predictive avoidance system. This certainly would be something that could peek the curiosity of long distance fleet operators and at the least the insurance industry generally.

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