Trucking accidents are pretty common in the U.S., resulting in thousands of fatalities each year. That’s why various measures are being taken to reduce the number of trucking accidents. Predictive analytics is one such measure that has proven to be quite effective at preventing these accidents.
Trucking companies can get much use out of predictive analytics. The predictive analytics technology can be used by companies to asses truckers’ driving abilities during the recruitment process, and determine potential risks of hiring a certain job applicant. There are several predictive analytics models, with some of them being able to determine driver’s fatigue levels, review and analyze driver’s employment history, as well as various aspects of a driver’s behavior related to truck driving.
So, basically, by using predictive analytics, you can determine if a driver is too tired to drive, which is very important, since fatigue is one of the most common reasons for trucking accidents. You can analyze drivers’ schedules and how much sleep a driver gets over the course of the workweek, so that you can make changes into their schedules as needed, and prevent sleep-deprived drivers from getting behind the wheel.
Also, this method can help companies get an idea of how good an employee a potential candidate is, by reviewing and analyzing their previous engagements, whether they have been able to hold on to a job for a long period of time, so you’ll know how reliable they are, and whether you can expect them to stay longer with your company, or it’s more likely that they are going to want to switch companies soon after you hire them.
These predictive models are intended to help companies recognize certain harmful practices some of their drivers may do, that can jeopardize their safety, and act on it accordingly. Apart from that, trucking companies can also improve their drivers’ productivity this way. They can improve fuel efficiency, and optimize drivers’ schedules, by analyzing drivers’ habits.