Latest revision as of 02:30, 23 March 2021

Abstract

In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes. However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety. The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand, speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions, which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving off-peak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.

Document type: Article

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• https://link.springer.com/content/pdf/10.1007/s40534-018-0166-x.pdf under the license https://creativecommons.org/licenses/by

• http://link.springer.com/article/10.1007/s40534-018-0166-x,

https://doaj.org/toc/2095-087X,

https://doaj.org/toc/2196-0577 under the license cc-by

• http://link.springer.com/article/10.1007/s40534-018-0166-x/fulltext.html,

http://link.springer.com/content/pdf/10.1007/s40534-018-0166-x.pdf,

http://dx.doi.org/10.1007/s40534-018-0166-x

• https://link.springer.com/content/pdf/10.1007/s40534-018-0166-x.pdf,

https://link.springer.com/article/10.1007/s40534-018-0166-x,

[=citjournalarticle_623079_34 https://www.safetylit.org/citations/index.php?fuseaction=citations.viewdetails&citationIds[]=citjournalarticle_623079_34],

https://academic.microsoft.com/#/detail/2809407956 under the license https://creativecommons.org/licenses/by/4.0