Risk drivers pose to themselves and other drivers by violating traffic rules

ABSTRACT

Objective: Violation of traffic rules is a major contributing factor in both crashes and fatalities in the United States. This study aims at quantifying risk that drivers pose to themselves and other drivers by violating traffic rules.

Method: Crash data from 2010 to 2013 were gathered for the state of North Carolina. Descriptive analysis was carried out to identify frequent traffic violations and who were committing the traffic violations that resulted in crashes. A multinomial logit model was then developed to examine the relation between different traffic violations and driver injury severity. Additionally, odds ratios were estimated to identify the likelihood (probability) of severe or moderate injury to the driver and other drivers due to a driver violating a traffic rule that led to a crash.

Results: Exceeding the speed limit is more likely to result in severe injury compared to disregarding traffic signals. However, going the wrong way is more likely to result in severe injury to other drivers when compared to any other traffic violation. Driving under the influence of alcohol is 2 times more likely to result in severe injury than driving under the influence of drugs. These 2 traffic violations by a driver are almost equally likely to result in severe injury to other drivers.

Conclusions: Drivers often perceive that violating traffic rules will not result in a crash or severe injury. However, the results from this study show that a majority of the traffic violations lead to severe injury to the violator as well as to other drivers. The findings from this study serve as documented evidence to educate drivers about the risk they pose to themselves and to other drivers by violating traffic rules and encourage the adaptation of safe driving behavior in order to contribute toward reaching the “zero traffic deaths” vision. They also help make policy changes pertaining to penalty points and fines for violating a traffic rule.

KEYWORDS:

• Traffic violation
• driver
• injury
• crash
• risk
• multinomial logit model
• odds ratio
• speeding

Acknowledgments

The authors appreciate the assistance and support of the staff of the Highway Safety Information System (HSIS) with the crash data required for this research. Special thanks are extended to Anusha Patel Nujjetty, HSIS Lab Manager.