Accident Severity Model helps fleets leverage HOS data

Omnitracs announced the introduction of its Accident Severity Model to take the data collected via compliance-centric Hours of Service (HOS) applications and use it to manage risk.

Designed for any fleet using an electronic HOS management system, regardless of size, Omnitracs said its Accident Severity Model “identifies the highest risk drivers who are most likely to be involved in a serious accident, thereby enabling proactive intervention to prevent accidents, reduce related expenses and enhance overall safety.” Based on a 10-year study of nearly 208,000 crashes, the Accident Severity Model accurately predicted 90% of severe accidents in the 50% of drivers deemed to be at highest risk.

“With the ELD mandate imminent, fleets are acutely aware of the need to operate in accordance with current and pending regulations,” said Dean Croke, vice president of analytics. “Among the positive outcomes of the increased focus on hours of service management is the fact that many in the industry also recognized the critical importance of looking beyond basic compliance to ensure the safe operation of trucks on our nation’s roads. What many in our industry don’t realize is that it’s possible to be 100 percent compliant with HOS regulations and be sound asleep at the wheel, simultaneously. This is why we’ve developed the Accident Severity Model—to allow fleets to predict serious accidents using HOS data and proactively intervene with those drivers at highest risk for being involved.”

At launch, the model relies on data to measure true performance rather than relying on drivers’ perception of their condition and alertness, Omnitracs said. The company added that the model predicts drivers likely to have an incident based on both loss of control, in which there is a complete disconnect from the driving task, and distraction—or lack of concentration. Coming soon, it will feature two-way technology that pulls data from the HOS application and returns safety measures to the driver, essentially serving as a virtual co-driver, according to the company.

“Data shows that the average severe accident cost has a very wide range due to the random nature of the event from $250,000 to $1 million, not including injury, damage to a fleet’s reputation or long-term impacts associated with higher insurance premiums, making the financial benefits of preventing such incidents obvious,” Croke said. “Omnitracs’ Accident Severity Model allows fleets to predict accurately a significant proportion of crashes, thereby reducing expenses and mitigating resulting outcomes up to and including loss of life. If a carrier prevents only one severe accident in five years, a fleet of 25 trucks would experience a return of more than 10 times its investment in the solution.”

Omnitracs said its Accident Severity Model allows fleets of all sizes to benefit from algorithms and technology to improve safety outcomes. Any fleet generating Hours of Service data can implement this solution to prevent frequent, low-value claims and infrequent, but high-value claims—both of which can significantly damage the bottom line.