Fleet safety shifts from reaction to prevention

Knowing which drivers are most at risk of an accident weeks before it happens could fundamentally change the way transport operators approach fleet safety. Using predictive models to analyse driving behaviour, operators can increasingly identify emerging risk patterns and intervene before they result in an accident. According to Allen Harington, head of sales South Africa at Ctrack, this represents an important shift away from simply analysing past incidents. “You can’t prevent yesterday’s accidents, but you can learn from them. You can identify the drivers most at risk. You can intervene before the next one.” Speaking at a recent Transport Forum, Harington said predictive technology was increasingly allowing transport operators to use existing telematics data to identify behavioural patterns associated with crashes and intervene earlier. “AI is not replacing transport professionals. AI is helping them make better decisions faster,” he said, indicating there was still some apprehension in parts of the market around adopting AI models. “We expect this to change as confidence in the technology grows. As these models become more trusted within the industry, we predict that by 2032 AI will help transporters across the country make faster decisions without all the noise that data can put on the table.” He said fleet intelligence was moving beyond traditional dashboards that showed vehicle location and status towards systems capable of providing context, identifying causes and recommending what action should be taken next. “The next leap is not more screens, it’s turning live fleet data into clear judgement at operational speed.” Harington said AI was already being applied across transport operations to improve safety, reduce fuel costs and identify potential vehicle failures, delivery delays and security risks. In South Africa, it could also help identify unusual behaviour and patterns associated with vehicle theft and hijacking. One of the most significant developments is the ability to build individual driver risk profiles using large volumes of historical driving and claims data. Rather than focusing only on traditional telematics events such as harsh braking or speeding, predictive models could analyse thousands of smaller behavioural indicators, including acceleration, speed consistency, braking anticipation and erratic steering corrections. “Every micro-acceleration, subtle speed change or hesitation in braking creates a unique behavioural signature,” said Harington. These patterns can then be compared with historical crash data to establish a driver’s probability of being involved in a future accident. This allows fleet operators to rank drivers according to risk and, importantly, target interventions rather than applying the same training approach across an entire fleet. “It’s no longer shooting in the dark,” said Harington. “Each driver profile really zooms into what the specific driver battles with.” A driver showing signs of distracted driving, speed fluctuations or excessive lane movement, for example, could receive coaching specifically addressing those behaviours. Predictive technology can also track whether a driver’s risk profile is improving or deteriorating and provide feedback on individual trips. For fleet managers, this means being able to identify which drivers require intervention weeks before an accident might occur. LV