15 February 2023

Why well-being matters in driver safety management

Man is smiling and while driving

Elevate health management with AI to boost productivity

Managing safety is a number one priority for many fleets and mobility companies. Increasingly, those involved in driver safety management are also realizing the importance of driver well-being and recognizing the link between health, safety, and productivity. The challenge is how to measure and understand driver health while respecting driver privacy. Artificial intelligence (AI) is providing new insights into driver performance and can elevate the management of driver well-being to another level.  

Download now: Five ways to use your GPS data

Managing driver safety is good for business

Comprehensive risk management is fundamental to business success for all stakeholders in mobility – including fleets, insurers, new mobility managers, etc. Reducing driver risk not only lowers the likelihood of crashes, injuries, damage, costs, etc, but prioritizing safety is also good for company reputation, employee engagement, and productivity. 

For some time now, telematics solutions have been used to measure driver behavior. AI goes a step further by “filling in the gaps” between driver events and adding an extra layer of knowledge about driver attitude. Risk management professionals can utilize AI to predict crash probability and understand the reasons behind it, including levels of focus and anticipation.  

Elevating the focus on driver well-being

Incorporating driver well-being into workplace safety programs can deliver additional value. Research from around the world shows that increased engagement in the workplace results in improved employee satisfaction and performance. And, of course, the more insight a risk manager has about drivers’ on road performance (and the reasons behind that performance), the more relevant and efficient any risk intervention is likely to be. 

For people who drive for work purposes, lifestyle is an important consideration. Those who drive long hours, for example, might be at risk of loneliness, lack of exercise, and poor diet. Anything that impacts on mental health can affect driving risk level. This might include poor reaction times, lack of focus, unnecessary variations in speed, and more.  

Uncovering a new level of insight

With AI, anyone involved in risk management can access deeper insights into driver attitude and start understanding how well-being relates to risk level. For example, at Greater Than, we can turn GPS data into a Crash Probability Score and can also provide insights into how the score has been generated. 

At the driver level, it’s possible to see how the attitude of the driver influences risk level, and how attitude changes over time. Further evaluation – and discussions with the driver – can uncover the contributing factors resulting in attitude change, including well-being factors such as stress, fatigue, and other personal factors. Imagine how insight into sleep quality, for example, could help to prevent fatigue-related collisions with the intervention of relevant training 

Taking the relationship between driver health and road safety further

Already, technology is capable of taking the management of driver health and well-being to another level. Greater Than recently conducted a major study into health and safe driving during a subevent of the FIA Smart Driving Challenge. The aim was to understand how factors such as fatigue, stress, and illness affect driving risk and crash probability.  

In the future, with driver consent, wearables could become a feature in driver risk management to help drivers understand how health can influence risk level before they even step inside their vehicle. Similarly, being able to identify health and well-being risk factors before they cause problems on the road could transform driver safety programs.  

It’s easy to uncover new levels of insight

All that’s needed to see deeper driver insights, including crash probability and potential causes, is GPS data, which can be shared via API, SDK, or a smartphone app connection. Once a fleet of drivers is connected to Greater Than’s AI platform, our database processes and analyzes every trip against billions of previous real-world trips using pattern profiling. 

The analyzed data can be pushed to a fleet’s existing driver safety management software, a driver app, or visualized via Greater Than’s Risk Portfolio Tracker. This provides actionable information to support well-being and safety objectives and help boost efficiency and productivity.  

Contact Greater Than or book a meeting with me to learn more about how AI can help you access deeper insights into driver crash probability and potential causes.