Our findings include the five risky behaviours seen most often among waste drivers, most improved driving behaviours, and insights on how waste-industry driving habits compare to those of other industries.
This data was captured from fleets of all sizes and types within the waste industry, including recycling, disposal and sanitation fleets.
Behaviours may appear on both the ‘most prevalent’ and ‘most improved’ lists. This demonstrates that even with significant improvement, fleets and drivers must stay vigilant and maintain awareness to keep those behaviours trending downwards.
Improvement in Late Response
Improvement in Driver Unbelted
We compared the prevalence of behaviours seen in waste fleets against behaviour averages of fleets across all of its other protected industries. Comparatively, waste fleets stood out in the following areas:
Smoking which occurred 22% more often
Driver unbelted, which occurred 22% less often
Lytx also found that 28% of high-impact waste-industry collisions were due to drivers driving too fast for the conditions. When it comes to low-impact collisions, nearly one-in-four were due to distractions, such as smoking, food & drink and cell phone/device use.
To better identify and address top areas of driving risk within their individual fleets, thousands of organisations use the best-in-class DriveRisk Driver Safety Program; these organisations experience on average up to 50% reduction in collisions and up to 80% on associated claims costs as a result.
These insights were derived from Lytx’s proprietary database of trucking driving data, including 6.67 million risky trucking driving events captured last year.
For comparisons across industries, we calculated behaviour averages from our global database, which contains driving data from utilities, distribution, concrete, construction, services, transit, government and waste industries.
Our database maintains the fastest-growing proprietary database of professional driving data in the world, currently surpassing 200 billion kilometres of driving data. The data is anonymised, normalised and in instances of behavior prevalence, is generalisable to trucking fleets at large.