How Contextual Data Can Help Fleets With Fuel-Efficient Driving

Each year, bad driving costs fleets an estimated $5,000-$6,000 in extra fuel per vehicle.* For a fleet of 500 vehicles, efficient and safe driving can save up to $3 million annually in fuel costs alone by reducing unnecessary idling, decreasing overspeeding and driving smoothly. On top of that, better driving behaviors can provide fleets with peace of mind from mitigating accidents. Contextual data is key for optimizing these behaviors, as it provides the insights necessary to promote efficient and safe driving.

What is contextual data?

By definition, contextual data provides frame of reference to an event. There are several types of contextual data that deliver value to the fleet industry:

• Weather (e.g., temperature, precipitation, visibility)
• Hazardous Areas (e.g., historical incidents, abnormal intersections, construction zones)
• Zoning (e.g., residential, industrial, rural)
• Roadway Info (e.g., highway, intersection, on/off ramp)
• Time of Day (e.g., day, night, weekend)

For example, if a driver is driving hazardously and the fleet manager knows he or she is driving in a non-hazardous area, the fleet manager can intervene and coach the driver accordingly. On the other hand, if the fleet manager knows the driver is driving in a hazardous area, they can provide that driver with insights on how to drive safely and efficiently while knowing ahead of time that some level of hazardous driving is unavoidable.

What does activating contextual data look like in practice?

Many fleets stop short by simply looking at counts of dangerous driving events — for example, “Driver X had six hard-braking events and two hard-acceleration events.” In order to maximize savings potential, fleets must go one step further to analyze, understand and take action on those events.

When drivers are exhibiting fuel-averse behavior — like speeding, excess idling and hard braking — fleet managers need to be smart about driver coaching to decrease these behaviors. Knowing that your driver is hard braking, but everyone around them is not, adds an extra layer of information to help fleet managers correctly identify the severity of these events and prioritize which drivers need additional coaching.

For example, on the surface it appears that the two drivers shown above are driving similarly: each driver performed two hard-braking events and one hard-acceleration event. What’s missing, though, is the context.

If you add in contextual data to show hazardous areas, you can see that Driver A is driving through areas where others are consistently driving hazardously — in other words, comparable to others who pass through the same area — whereas Driver B doesn’t have any hazardous areas in sight and therefore should be coached accordingly.

Taking this information into account, a fleet manager could choose to reward Driver A for driving well in a hazardous area compared to his or her peers. An operations manager could also look at this information and realize that they’re continuously directing drivers to drive through hazardous areas. They could take corrective action by directing fleets on a different route moving forward, providing in-cab coaching along the route, or coaching drivers beforehand on slowing down, increasing their following distance, and being more mindful and careful.

These are all ways that fleet managers can improve their fleet’s fuel economy. It’s key to understand that different events require very different types of coaching depending on the contextual data being used.

What’s the impact on fuel from these events?

Based on a fleetwide analysis of a mid-size trucking company*, drivers that had at least one hard-braking event or one hard-acceleration event worsened their fuel economy by 20% on average. Contextual data helps us understand that not all poor driving behaviors are avoidable, but cutting out the behaviors that are avoidable can save fleets approximately $6,000 per vehicle annually.

At Uptake, we use artificial intelligence to create products that help fleets run better by leveraging predictive maintenance, improving driver safety and fuel efficiency, and maintaining compliance.

The content of this blog post was presented by Uptake data scientists at Geotab CONNECT 2018.

* Source: Uptake Customer Research