Algorithms Based on Past Data
Predicting at-risk drivers starts with analyzing pre-accident data. The model estimates drivers prone to accidents and measures accuracy by comparing predictions to actual outcomes. Based on the results, it adjusts inputs and recalculates. Through countless iterations, the model ‘learns’, refining its focus on events leading to accidents.
Safety managers can modify these predictions considering specific situations. For instance, if an incident involving a driver wasn’t their fault, managers can reclassify that event, and the model adapts. If the driver’s response, though not perfect, was their best effort, adjusting the data’s classification alters its significance in the model.
As fleets collect more data, the model perpetually refines itself. Real-time driver data feeds into this model, alerting safety teams about potential risks before an accident occurs, giving safety teams the time needed to address risky behavior and get drivers back on the road safely.
Driver Watch List
Once the integrated data runs through the ML models, the system determines which drivers are at-risk for an accident and assigns them a score. The Idelic Watch List is simple, intuitive, and includes details on a driver’s safety events, risk level, and breakdown of their score. These processes occur behind-the-scenes so that safety teams can leave the technical jargon aside and easily make sense of the data.
ML processes enable safety managers to pinpoint who their at-risk drivers are and which safety events are causing a low score. With proprietary ML techniques, this type of data can be easily displayed by percentages, which are a powerful tool for understanding the cause of the driver’s increased risk and analyzing the best course of action for coaching.
Performance Improvement & Accident Prevention
The actions taken to prevent accidents are just as critical as the ML Watch Lists that are predicting at-risk drivers. Driver Watch Lists not only display the most at-risk drivers but also help identify potential behaviors that need correction before an accident occurs.
Assigning targeted Professional Development Plans (PDP) based on specific driver events and behaviors is vital to preventing accidents. The Safety Suite Platform allows users to assign targeted training and coaching, manage dates and timelines efficiently, and carefully track driver improvement.
When a group of drivers incurs similar safety events, PDP templates allow safety managers to quickly implement training and improve driver safety across an entire fleet. If a driver has a problem unique from the rest, tweaking plans for specific training needs is easy. The ML model’s continuous analysis of driver risk scores allows for the most up-to-date and accurate Driver Watch List.