Reduce Costs and Increase Safety through Proactive and Targeted Risk Avoidance
Contrary to conventional wisdom, no single data point can be used to accurately predict future events on a consistent basis. Omnitracs Predictive Analytics technology uses all available data across thousands of data points to build a true picture of the driver's behavior and find the trends that matter.
Further, this data is used to create a comprehensive image of driver performance, and more importantly, a map to detect the changes in behavior that are proven to indicate higher accident risk.
Benefits of Risk Mitigation
Omnitracs Predictive Risk Mitigation Analytics Brochure
Accident Severity Model
Omnitracs Predictive Analytics modeling and remediation strategies help you identify the highest risk drivers who are most likely to be involved in a serious collision, so you can prevent accidents, reduce related expenses, and enhance overall safety. With proven results, based on a 10-year study of nearly 208,000 crashes, the Accident Severity Model accurately predicted 90 percent of severe accidents in the 50 percent of drivers deemed to be at highest risk. This proves that the ASM will help fleets avoid injury, reputation damage, the long-term impact of higher insurance premiums, and any other financial burdens that severe accidents place on fleets. In the end, the Accident Severity Model will allow your fleet to gain a demonstrable ROI and, most importantly, save lives.
Predictive Safety Model
The vast majority of accidents are not random, but the result of a gradual change in driver behavior, impacted by on- and off-the-job factors.
Omnitracs Predictive Analytics analyzes the big data your fleet is already generating to build a predictive model that monitors thousands of data points for the hidden, yet impactful, trends that serve as predictors to accidents.
Omnitracs Predictive Analytics' Safety Model is proven to help fleets reduce and prevent accidents. The graph to the right illustrates the influence early intervention can have on accident frequency and presents a side-by-side comparison of remediated and non-remediated drivers. The results speak for themselves, proving predictive analytics reduces accidents, keeps drivers safe, and slashes costs.
Driver Fatigue Model
A commercial truck driver can be sound asleep at the wheel and simultaneously be 100 percent compliant with Hours of Service regulations, and only through a deep understanding of what drives fatigue risk can managers and drivers reduce accident severity.
With Omnitracs Analytics’ Driver Fatigue Model, fleets can quickly identify high fatigue risk and accident severity probability.
Omnitracs Analytics uses data from the four levels of driver duty status in a driver’s log to derive over a thousand individual data points for modeling fatigue risk, including those in the chart to the right.
In a six-month study of 1,765 over-the-road drivers (50 percent of the total population) using the Driver Fatigue Model and Sleep Management Program, one Omnitracs Analytics customer reported:
- 7.2 times lower accident severity cost
- Four times fewer “loss of control” accidents (rollover, sideswipe, and jack-knife)
- Five times fewer “left highway” accidents
- 72 percent reduction in rollover accident rate
Workers' Compensation Model
In many ways, the trucking industry has accepted workers' compensation claims as a cost of doing business. Until now, training and process improvements have been used as the only corrective actions, however, these do not impact claim rates. Omnitracs Analytics brings a new approach to managing workers' compensation, by identifying the driver behavior that leads to claims. Omnitracs Analytics analyzes fleet data and detects the behavior changes that signal a driver is likely to make a claim.
Omnitracs Analytics analyzes thousands of data points to identify the subtle changes that collectively represent a pattern of driver behavior that lead to workers' compensation claims. This analysis allows for possible prediction and prevention of a significant number of compensation claims from ever being filed.