One Rear-End Crash Could Destroy Your Motor Transport or Service Business

TASA ID: 9075

If you own or operate any kind of work vehicle fleet, whether a truck transport fleet or a non-transport service fleet, you should be aware that liability from rear-end road crashes is among the biggest threats you face.  Road crashes are a looming threat to truck transport companies, including delivery operations.  They also threaten the economic welfare of light vehicle service (e.g., taxi, repair, installation) fleets in metropolitan areas or other regions.

Motor vehicle crashes are the leading cause of death among U.S. workers (NIOSH, 2003, 2015) .  Our target in this discussion is rear-end (RE) crash impacts where the transport/work vehicle is the striking vehicle.  The U.S. DOT Large Truck Crash Causation Study (LTCCS) found that about 10% of all serious injury or fatal truck crash involvements were of the RE-striking type (Knipling, 2009).  These crashes may injure truck drivers but they cause much more damage to struck light vehicles and more human harm to their occupants.      

Rear-End Crash Harm

For large trucks, in particular tractor-semitrailers, the “Big Three” at-fault crash types are single-vehicle crashes, lane change/merge strikes, and RE strikes (Knipling, 2009).  Each type is worst in its own way.  Single-vehicle truck crashes kill the most commercial drivers, due in part to the physical force of rollovers and/or to safety belt non-use.  Lane change/merge scenarios are where big rigs as the encroaching vehicle are most involved compared to other vehicle types.  That’s largely due to limited driver visibility around trucks, especially on the right side.

Yet it is RE-striking crashes that cause the most overall human and material harm, and create the most tort liability for companies.  I estimate that the expected vehicle lifetime risk of one tractor-semitrailer is about $22,000 for rear-end strikes alone.  This estimate is based on a 1990s analysis (Wang, Knipling, and Blincoe, 1999) extrapolated to today’s dollars.  The number encompasses all economic loss from crashes (e.g., damage, medical, lost income) as well as monetized values of human suffering, such as reduced quality of life.  Monetized human suffering is also a major element of large crash liability settlements and verdicts.  If your large fleet buys 100 truck tractors this year, you can expect roughly 100 × $22,000 = $2.2M in comprehensive harm from rear-end striking impacts involving your 100 vehicles in their operational lifetimes.  Lifetime loss numbers are lower for light trucks/vans, but still considerable.  My statistics suggest about $6,900 per vehicle, or $690,000 for a fleet of 100 light trucks/vans.  While most work vehicles are never involved in a serious RE strike, those that are involved rocket average losses up to these sky-high levels.  You must make efforts to prevent these crashes.

There are two obvious vehicle roles in RE crashes: the striking vehicle and the struck vehicle.  With some exceptions, drivers in RE-striking impacts are considered at-fault and held liable, while struck vehicle occupants are seen as innocent victims.  In litigation, struck vehicle occupants are plaintiffs while striking vehicle drivers and their companies are defendants.  The American Transportation Research Institute (ATRI) has published two recent studies on truck crash litigation (Murray et al., 2020; Evans & Leslie, 2021).  In Murray’s study of million dollar-plus cases, 89% of 66 RE crashes ended with findings for the plaintiff.  In contrast, when the truck was changing lanes, passing, or merging, only 44% of cases resulted in plaintiff findings.

Murray’s study was limited to million dollar-plus cases, whereas the Evans study looked at those less than a million.  In Murray, RE crash verdict sizes (59 plaintiff, 7 defendant) averaged $5.46M.  In Evans, RE crash payments averaged $428K, with settlements ($511K) costing more than jury verdicts ($359K).  Payments like this can bankrupt companies, either directly or eventually due to increased insurance rates.

Public safety messages tell us that children in vehicles are almost always safer in the back seat.  Yet the opposite is true when children are in the back seat of a rear-struck vehicle.  Per Murray, when children are injured in major crashes, mean verdict size rises 18-fold.  It’s the biggest single factor affecting award sizes.

One of my expert witness cases involved an asleep big rig driver striking a Kia stopped in a nighttime traffic backup on an Interstate.  The impact speed was 65mph.  The Kia’s three occupants were a father, mother, and seven-year-old boy in the back seat.  The “extremely violent impact” crushed the Kia and exploded it into flames.  All three family members died instantly.  In the cataclysm a third vehicle was also struck and engulfed in fire.

Causation

Tailgating is the primary cause of RE crashes.  True or false?

The statement is false.  The most frequent proximal cause of RE crashes is driver inattention of various types.  This is especially true of RE crashes where the lead vehicle is stopped prior to the impact.  Such RE Lead-Vehicle Stopped (RE-LVS) crashes typically occur when the lead vehicle is stopped at an intersection or on the road waiting to turn left.  The schematic shows a typical truck-strikes-car scenario.  Instrumented vehicle studies (e.g., Lee et al., 2007) show that striking drivers usually have plenty of time (2+ seconds) to recognize the stopped vehicle and evade an impact.  Yet often they are distracted due to cell phone use, reaching for something inside the vehicle, or a distraction seen outside the vehicle.  Sometimes striking drivers are asleep, as in the horrific crash described above.  For large trucks, degraded brakes may play a role.

There’s a second, less frequent, category of RE strikes: those where the lead vehicle is moving at a slower speed or decelerating.  These are called RE Lead-Vehicle Moving (RE-LVM) impacts.  For this type, tailgating (following too closely) plays a relatively larger role, though direct driver observations (Lee et al., 2007, Carney et al., 2015) show inattention to still dominate.  Excessive speed (over the speed limit or excessive for conditions) also contributes to many RE-LVM impacts (Knipling, 2009).

In the LTCCS, the striking truck was at-fault in 99% of RE-LVS impacts but only 84% of RE-LVM impacts (Knipling and Bocanegra, 2008).  Why the different percentages?  A common misbehavior of light vehicle drivers on highways is to cut in front of large trucks.  A resulting RE-LVM crash might then be rightly blamed on the driver who cut in.  In a few cases, cutting-in drivers have even been charged with attempted criminal fraud.  Investigations, perhaps using video footage from the truck, can sometimes show that they intentionally caused the crash to seek a large litigation payment (Murray et al., 2020).  You could put that in the Stupid Criminal file, though sometimes they get away with it.

Carney et al., (2015) viewed videos of 229 vehicle-to-vehicle crashes involving work fleet (e.g., taxi, shuttle, delivery, service) drivers.  All were moderate-to-serious crashes where the work driver was primarily at-fault.  The work fleets had been instrumented with the Lytx DriveCam^® in-vehicle monitoring system.  Such systems capture video and sensor evidence relating to crashes and other abrupt (e.g., hard braking) events.  Researchers classified the crashes as either RE (101) or angle impacts (128).  They also viewed 8 seconds of each pre-impact video to observe and classify driver errors leading to the crash.  More than one error could be cited.  Almost all (97%) of the RE strikes were preceded by work driver recognition failures; i.e., inattention to the road ahead.  Additionally, following too closely was cited for 27%.  That’s a driver decision error.  In other words, the driver made a volitional decision to tailgate.  Angle crash (e.g., crossing path) impacts were a little different than RE impacts.  Recognition failures were still the biggest driver error category (70%), but volitional decision errors (e.g., failure to yield, violating sign/signal) were also seen in 50%.

Driver recognition failures are overall the biggest proximal cause of traffic crashes (Starnes, 2006, NHTSA, 2008, Knipling, 2009).  They include various forms of driver inattention to road and traffic events.  In Carney, 64% of RE-strike drivers were engaging in some extraneous activity such as using their cell phone or reaching for an object in the vehicle.  The inattention total rose to 97% when researchers included eyes-off-road and apparent cognitive capture (driver “lost in thought”).  Compared to angle impacts, RE-striking drivers had their eyes off the road four times as long in the pre-impact seconds.  They were also 3.5 times more likely to be using cell phones.

Prevention

The obvious purpose of fleet safety management is to prevent crashes.  Serious crashes are tragic and traumatic events, even for those not severely injured.  Yet a second purpose, perhaps cynical, is to look good in court should an at-fault crash occur.  This may reduce punitive payments stacked on top of compensatory payments to victims (Murray et al., 2020).  Here are four proven ways to reduce RE-striking and other motor fleet crash risks:

1. Systematic fleet safety management program.  Core functions of fleet safety management include hiring and selecting safe drivers, orientation and training, ongoing safety communications (e.g., meetings), continuous driver monitoring & evaluation, recognitions and rewards for safe performance, discipline and/or remediation for unsafe actions, investigating accidents for lessons learned, and always striving for improvement.  These and related management practices constitute safety culture.
2. Firm cell phone policy.  At a minimum, reinforce state laws and federal regulations for commercial vehicles.  Consider even stricter rules.  For example, hands-free cell phone use may be legal for your drivers, but it can still be dangerous, especially in town or urban traffic.  Driving simulator tests demonstrate five types of driver cognitive and performance deficits associated with hands-free cell phone conversations.  They include deficits in visual scanning, predicting unfolding events, recognizing hazards, deciding how to respond, and executing that response (Strayer & Fisher, 2016). 
3. In-vehicle monitoring.  Safety-conscious fleets monitor and evaluate their drivers continuously and comprehensively (Toledo et al., 2008).  Continuous onboard safety monitoring can record and assess driver speeds, hard-braking, lateral accelerations (indicative of speed on curves), lane keeping, idling times, and fuel economy (a good surrogate of safety).  Earlier we presented research (Carney et al., 2015) based on work vehicles equipped with Lytx DriveCam® devices.  These and similar devices capture abrupt driving events, both crashes and near-crashes.  Near-crashes are a leading indicator of a driver’s crash risk.  Safety directors can use the videos and data as a sit-down training tool with drivers.  In more dire cases, it is a solid basis for driver discipline or termination.  Safety directors also report that vehicle-mounted videos often exonerate their drivers by showing that the other driver was at-fault.
4. Forward Collision Warning (FCW) systems.  Dramatic RE crash reductions are possible through the application of advanced onboard safety technologies, such as Forward Collision Warning (FCW) systems.  FCW systems use radar or other sensors to monitor the forward roadway and warn of rapid closing with a vehicle or object ahead.  Most often they initiate warnings quickly enough to evoke a successful driver avoidance response.  They prevent most RE-strikes and reduce the impact severity of those that do occur.  Some systems even initiate immediate de-throttling and/or braking.    The Insurance Institute for Highway Safety (2020) found that FCW systems reduce RE-striking injury crashes by 56%.

All of the safety interventions presented here have positive Returns-on-Investment (ROIs) for fleets.  Fleet safety is not just morally correct – it’s profitable!   

Cited Refernces

Carney, C., McGehee, D., Harland, K., Weiss, M., and Raby, M.  Using Naturalistic Driving Data to Assess Vehicle-to-Vehicle Crashes Involving Fleet

Drivers. Univ. of Iowa and AAA Foundation for Traffic Safety.  June 2015.

Evans, C. and Leslie, A.  The Impact of Small Verdicts and Settlements on the Trucking Industry.  American Transportation Research Institute (ATRI), November 2021. Available at truckingresearch.org.

Insurance Institute for Highway Safety (IIHS).  Information sheet:  Real-world benefits of crash avoidance technologies.  Available at www.iihs.org.  December 2020.

Knipling, R.R.  Safety for the Long Haul; Large Truck Crash Risk, Causation, & Prevention.  American Trucking Associations.  ISBN 978-0-692-00073-1, 2009a.  Available for purchase at www.atabusinesssolutions.com.

Knipling, R.R. & Bocanegra, J.  Comparison of Combination-Unit Truck and Single-Unit Truck Statistics from the LTCCS.  FMCSA & Volpe Center Project report.  Contract No. DTRS57-04-D-30043.  2008.

Lee, Suzanne E., Llaneras, E., Klauer, S., & Sudweeks, J.  Analyses of Rear-End Crashes and Near-Crashes in the 100-Car Naturalistic Driving Study to Support Rear-Signaling Countermeasure Development.  NHTSA Report No. DOT HS 810 846.  October 2007.

Murray, D., Williams, N. and Speltz, E.  Understanding the Impact of Nuclear Verdicts on the Trucking Industry. ATRI, June 2020.  Available at truckingresearch.org.

NHTSA. National Motor Vehicle Crash Causation Study: Report to Congress. DOT HS 811 059. U.S. DOT, 2008.

Pratt, S. National Institute of Occupational Safety & Health (NIOSH). Publication No. 2003-119: Work-Related Roadway Crashes – Challenges and Opportunities for Prevention; 4. Special Topic on Driver Fatigue, September, 2003.  Available at: http://www.cdc.gov/niosh/docs/2003-119/2003-119d.html.

NIOSH.  Preventing Work-Related Motor Vehicle Crashes.  2015.  Available At: Https://Www.Cdc.Gov/Niosh/Docs/2015-111/Default.Html.

Starnes, M.  LTCCS:  An Initial Overview.  NHTSA National Center for Statistics & Analysis, DOTR HS 810 646, August 2006.

Strayer, D. L. and Fisher, D. L., SPIDER: A Framework for Understanding Driver Distraction. Vol. 58, No. 1, February 2016, pp. 5–12.

Wang, J.S., Knipling, R.R., and Blincoe, L.J.  The dimensions of motor vehicle crash risk.  Journal of Transportation and Statistics.  Volume 2, No. 1, Pp. 19-43, ISSN 1094-8848, May, 1999.

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