Psychology

Distractions and emotions: key factors in vehicle collisions

Credits: IISG - CC BY-SA 2.0
by Jon Hankey | Director of Research and Development

Jon Hankey is Director of Research and Development at Research and Development, Virginia Tech Transportation Institute (VTTI), VA, USA.

Jon Hankey is also an author of the original article

Edited by

Massimo Caine

Founder and Editor-in-Chief

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published on Jun 30, 2016

In general, fatal crash rates have been declining in the U.S. for several decades.1 This improvement is due to a variety of factors, including the reliability and overall safety of vehicles and the design and conditions of U.S. roadways. However, the U.S. has not kept pace with other developed countries in terms of overall traffic safety.2 There are many potential reasons for this discrepancy, including driver behavior and performance.

Until relatively recently, we have not been able to fully understand the role of driver behavior and performance leading up to a crash. To fill that knowledge gap, the Virginia Tech Transportation Institute (VTTI) pioneered - and continues to evolve - the naturalistic driving study (NDS) method. The NDS method involves instrumenting volunteers' vehicles with an unobtrusive suite of cameras, sensors, and radars that continuously and automatically collect real-world driving behavior and performance. This lets us observe actual driving during the minutes and seconds leading up to a crash - analyzed frame-by-frame when necessary - from multiple channels of video.3,4

However, previous naturalistic driving studies were scoped such that they captured a relatively small number of crashes.5 With the completion of the Second Strategic Highway Research Program (SHRP 2) NDS, we now have access to the largest light-vehicle, crash-only sample size to date, with more than 1,600 total crashes identified.6

Our goal with the current analysis was to use the SHRP 2 NDS data set to more definitively determine the risk of several key crash causation factors - including impairment, driver error, and distraction - along with their respective prevalence. Overall, we found that driver error, impairment, and distraction were found to contribute greatly to crash risk, with at least one such factor present in nearly 90 percent of the 905 injurious and property-damage (i.e., higher-severity) crashes selected for analysis. We formed a very clear picture that distraction continues to be a great detriment to driver safety, with distracted drivers at double the risk of a crash for more than half of their trips. We saw that some factors absent in previous research findings increased driver risk, including driving under certain observable emotional states, which increased driver risk nearly tenfold. The use of a handheld cell phone (talking on, texting, reaching for) increased risk by nearly four times that of model (alert, attentive, and sober) driving. We also saw that other factors largely reported before to increase driver risk - such as applying makeup or interacting with a child in the rear seat - were relatively benign or even protective in terms of risk.

These results are sobering. For one, while we as a population know that engaging in distracting activities while driving increases crash risk, this analysis shows we are still choosing to engage in such activities a huge percentage of the time. Such findings are important because we see a younger population of drivers, particularly teens, who are more prone to engaging in distracting activities while driving. If we take no steps in the near future to limit the number of distracting activities in a vehicle, those who represent the next generation of drivers will only continue to be at greater risk of a crash.

It was also interesting to see in this analysis the role that being overtly emotional can play in driver crash risk. Overall, we hope that these conclusions will better inform policymakers, driver educators, law enforcement agencies, vehicle designers, and the general public about the risks of various sources of impairment, error, and distraction so that appropriate actions can be taken to help reduce their occurrence.


Edited by:

Massimo Caine , Founder and Editor-in-Chief

Original Article:

Dingus T, Guo F, Lee S, Antin J, Perez M, Buchanan-King M, Hankey J. Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences. 2016;113(10):2636-2641. doi:10.1073/pnas.1513271113.

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