Using Big Data and Predictive Analytics to

Predict Which Truck Drivers Will Have an

Accident

Steve Banker

CONTRIBUTOR

I cover logistics and supply chain management.

Predictive analytics are being employed in interesting new ways to improve

safety. Telematics solutions have long monitored events like hard braking and

speeding to flag unsafe driver behaviors. But today, this driver event data is

being enriched with other data streams to actually predict the likelihood of a

specific driver having an accident.

Omnitracs’ Critical Event Reporting is one such solution. According to Kevin

Haugh, Chief Strategy and Product Officer for Omnitracs, “These are

sophisticated models that look at telematics safety events but also look at the

driver’s schedule. How long do they drive? Longer hours mean more fatigue. But

it is not just the hours, it is the time of day when those hours are logged.”

A company called SmartDrive Systems goes even further in their efforts to predict

accidents. They are enriching the telematics data with video feeds from road

facing and interior facing cameras. The company is combining asset sensor with

driver sensor data to do better driver safety analytics and ultimately make better

predictions.

For example, a telematics solution would capture a hard braking event and

classify it as a negative behavior. But SmartDrive’s CEO Steve Mitgang points

out. “That is not necessarily indicative of bad driving. What if a hard braking and

swerving event occurred so the driver could avoid an accident with a teenage

driver that swerved into his lane?”

SmartDrive Systems is a predictive analytics supplier whose solution is based on

a private cloud architecture. In other words, all of their customers’ data is

captured by the company; they have telematics and video on four billion miles

driven, of which they scored almost 200 million events. SmartDrive’s customer

agreements allow them to analyze all the customer data so they can continue to

improve their algorithms. “We are constantly tuning and improving our

algorithms,” Mr. Mitgang said.

The combination of telematics and video has allowed their scientists to better

interpret the telematics data. By reading the telematics data, and seeing what

happened, they were able to determine, for example, that turning more than 165

degrees within a certain turning radius and time window was a risky U-turn on a

roadway. SmartDrive also detected additional patterns to avoid false trigger

activations in large open areas such as parking lots and truck stops.

Is the video analysis real-time? “It is near real time. If our analysis of the data

depicts severe risk – for example, a collision or near collision – a fleet analyst can

be alerted in a couple of seconds, and can view the video right away.”

But the videos can also be used after the fact. If a triggering event is detected –

incidents such as hard braking, swerves, and unsafe turning – fleet analysts can

view the video to see if the driver was responding appropriately to a bad situation

or if distracted driving or too closely following another vehicle was a contributor

to the problem. This becomes a coachable event.

Mr. Mitgang emphasized coaching for correcting these problems. “Human

behavior is not clean,” Mr. Mitgang said. “The worst drivers one month may

perform well next month. Every day is different. Context matters, we don’t

necessarily know what is going on in the driver’s life. For the most part these are

really good, professional drivers.” And just like professional football players look

at the film on how they performed the day after a game, “truck drivers are

professionals too. Looking at how you performed to improve your performance is

just part of being a professional.”

After the fact analysis can have another benefit. In a litigious society, the video

can help to prove the truck driver was not at fault. Car drivers can’t see around

big trucks. If the car driver claims they had an accident because a truck swerved,

for example, the video may show the truck swerved to avoid something. “This can

help to exonerate trucking firms from false claims.”

SmartDrive’s customers may also elect to share the company’s analytics with

outside companies. ProSight Specialty Insurance, for example, is willing to offer

lower rates to carriers when data can substantiate that a culture of safety exists.

There are other areas where these kinds of analytics can save money. The

segment of drivers seen as most likely to be in a collision also consume over 7.5

percent more fuel according to their statistics. And because this is a private cloud

solution, there is an opportunity for maintenance departments to see how their

expenditures compare to other similar types of firms, and for operations

departments to see how they compare to peers in areas like idling and fuel

consumption.

While the trucking industry has the reputation of being somewhat slow to adopt

new technologies, when it comes to Big Data and predictive analytics, the

trucking industry is on the cutting edge.

http://www.forbes.com/sites/stevebanker/2016/10/18/using-big-data-and-predictive-analytics-to-

predict-which-truck-drivers-will-have-an-accident/2/#6c4500e1599e