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.


Introduction
Though the fatality rate per vehicle miles traveled (VMT) has reduced over the years, 32,719 people died in motor vehicle crashes in the United States during 2013. North Carolina has reported 1,299 traffic fatalities and more than 100,000 injuries in 2013 (National Center for Statistics and Analysis 2014). It stands fourth in the number of fatalities when compared to other states.
To improve safety and mobility on the roadways, policy makers and transportation system managers proposed a set of traffic rules. Drivers are charged with penalty points and fines through enforcement practices for violating the traffic rules. Crash reports still indicate that violation of these traffic rules is a major contributing factor in both crashes and fatalities in the United States. Crashes involving speeding and driving under the influence of alcohol together accounted for 58% of the total fatalities in the United States (National Center for Statistics and Analysis 2015). During 1999 and 2000, around 1,990 and 1,294 people were killed at intersections for not obeying traffic signals and failing to yield the right of way, respectively, in the United States (Campbell et al. 2004). According to the Insurance Institute for Highway Safety, red light violations are the leading cause of urban crashes in the United States.
Numerous studies were carried out in the past on examining the role of driver characteristics in violating traffic rules or at specific locations. Moyano-Diaz (1997) measured the attitude of people toward traffic violations in Santiago. The study concluded that men are more risk takers than women and the same was stated by Yagil (1998) and Gonzalez-Iglesias et al. (2012). Shinar et al. (2001) stated that seat belt usage is positively correlated with age and level of education. Fosgerau (2005) found a significant correlation between driving speed and driver age as well as driving speed and driver income. Machin and Sankey (2008) indicated that excitement, altruism, risk-taking attitude, and their own likelihood of being involved in a crash accounted for 39% of young drivers' speeding. Braitman et al. (2007) identified that failing to yield the right of way increases with age. Factor et al. (2008) concluded that social habits and technological advancements play a major role in varied crash risk. Yamamura (2008) concluded that informal restrictions are more beneficial than formal legal restrictions in reducing dangerous driving behaviors. Zhang et al. (2013) examined specific risk factors that are associated with traffic violations and crash severity. Abdel-Aty (2003) used ordered probit models to analyze driver injury severity at different locations and concluded that drivers' traffic violations were significant in causing severe injuries at signalized intersections. Waller et al. (1986), Stoduto et al. (1993), Li et al. (1997), Cunningham et al. (2002), and Drummer et al. (2004) studied the effect of alcohol on driver injury severity in crashes. Retting et al. (2003) analyzed motor vehicle crashes at 2-way stop-controlled intersections. Stop sign violations accounted for 70% of the total crashes considered in their study. Drivers less than 18 years old and older than 65 years were found at fault in crashes that occurred due to violating stop signs. Pai and Saleh (2008) examined motorcyclists' injury severity at intersections using ordered probit models and concluded that motorcyclists are seriously injured when right-turning vehicles fail to yield the right of way. Zhou et al. (2015) observed that approximately 80% of wrong-way driving crashes occur in urban areas and nearly 70% of wrong-way driving vehicles are passenger cars. More than 50% of wrongway drivers were found to be under the influence of alcohol or drugs. Shankar and Mannering (1996), Carson and Mannering (2001), Khorashadi et al. (2005), Islam and Mannering (2006), Savolainen and Ghosh (2008), Malyshkina and Mannering (2008), and Geedipally et al. (2011) used multinomial logit models for assessing driver or crash severity but did not focus specifically on traffic violations. Ayuso et al. (2010) examined the influence of traffic violations on the likelihood of resulting in a serious or fatal crash using data for Spain. Some of their conclusions sound contradictory and are not applicable to all countries or locations. Additionally, traffic violations such as right turn on red, improper lane change, improper lane use, operating defective equipment, driving under the influence of alcohol, and driving under the influence of drugs were not considered in their study.
Overall, driver injury severity has been extensively studied for various crash types and under different situations. Research efforts such as Goldenbeld et al. (2013) showed that an increase in traffic offense frequency (number of violations) coincides with a stronger increase in relative crash involvement. However, literature documents no research on the extent to which drivers are injured in crashes due to different traffic violations. Educating and creating awareness about potential crash risk drivers pose to themselves due to violating traffic rules may lead to a reduction in the number of crashes and contribute toward reaching the "zero traffic deaths" vision. Unarguably, traffic violators pose a risk not only to themselves but also to other road users. These other road users may include drivers of other vehicles who did not violate any traffic rules, passengers in the vehicles involved in the crash, pedestrians, or bicyclists. The average vehicle occupancy is less than 1.5 and more than two thirds of the vehicles on major roads in the study area are single-occupant vehicles; driver only (PBSJ 2004). Furthermore, several crash records have missing occupant (passenger) details (injury severity type, gender, age, seat position, etc.). Considering multiple vehicle crashes with more than one driver violating traffic rules may add complexity and have a profound effect on the estimated risk. Accounting for these limitations, only drivers of other vehicles who did not commit any traffic violations were considered in this research. Overall, this study aims to quantify (1) the risk drivers pose to themselves (in terms of driver injury severity) by violating traffic rules and (2) the risk drivers (traffic violators) pose to other drivers who did not violate any traffic rules.

Method
Crash details are typically collected and reported by the law enforcement officers in the field. The reports are then entered into a crash database by the responsible state or local transportation agency staff. Any validation or reconstruction of a crash is performed by law enforcement and state or local agencies. The Highway Safety and Information System (HSIS) staff gathers all relevant data from selected state and local transportation agencies to develop and share the comprehensive database.
The crash data used in this research were gathered for the state of North Carolina from 2010 through 2013 from HSIS. The crash information is provided in 4 different files: accident, roadway, vehicle, and occupant files. These files are connected using the unique identification number provided for each crash. Council and Nujjetty (2014) summarized the variables and related details pertaining to information available in the HSIS database. The contributing factor, driver age, driver gender, injury severity type, lighting condition, and number of vehicles involved in each crash were only considered in this research. Except for the aforementioned variables, all other variables were deleted from the database. Crashes that have missing values or information were also removed from the database.
There were a total of 643,051 crash records for the study period. The data were processed so that each row represents a vehicle involved in the crash. Data obtained showed that 1,063,093 vehicles were involved in these crashes. Crashes in which more than 3 vehicles, pedestrians, or bicyclists were involved were not considered in the analysis (their contribution was less than 2% of the total crashes). The vehicle file provides the contributing factor for each vehicle involved in the crash. Consider an example in which a left-turning vehicle at an intersection did not yield to the through traffic and ended up in a collision (crash) with a through vehicle. In this case, the left-turning vehicle's contributing factor is recorded as "failing to yield the right of way" and the other vehicle's contributing factor is recorded as "no contributing factor. " In this case, the driver in the left-turning vehicle violated the traffic rule and put him-or herself as well as the other driver and passengers at risk. When the through vehicle exceeded the authorized speed limit, the other vehicle's contributing factor was recorded as "exceeded authorized speed limit" and the violation of the left-turning vehicle was still the same. The risk to the drivers and passengers of both the vehicles in the latter case might be affected due to the additional violation of the through vehicle. Such multiple violations in a crash are not considered in the analysis to accurately assess the risk of a traffic violation to the driver and other drivers. For the 4-year database, around 25,000 crashes occurred in which more than one driver involved in the crash committed some type of traffic violation. These were removed from the database and ignored for further analysis.
A driver may have violated multiple traffic rules (e.g., exceeded the speed limit and disregarded road signs) that may  have led to a crash. If all such combinations are considered, there would be more than 23 * 22 2 combinations. Therefore, only the primary contributing factor by the driver that led to a crash was considered for analysis. This would minimize any ambiguity that could arise due to the effect of different driver contributing factors in a crash. Records were also removed if the traffic violation was not in the list shown in the first column of Table 1. Overall, records of the drivers who violated traffic rules listed in Table 1 were only extracted and considered for analysis. This final data set to assess the risk drivers pose to themselves due to violation of a traffic rule consisted of 227,504 records (information pertaining to 227,504 drivers who violated traffic rules).
To examine the risk drivers (traffic violators) pose to other drivers (who did not violate any traffic rule), only 2-vehicle crashes were considered (more than 2-vehicle crashes contribute to less than 5% of the total crashes). A total of 350,180 2-vehicle crashes occurred from 2010 to 2013 in North Carolina. In 21,143 crashes, more than one driver violated some kind of traffic rule. These were removed from the database. Therefore, during the study period, 329,037 2-vehicle crashes occurred in which the driver of only one vehicle committed some kind of traffic rule violation. This implies that at least 329,037 drivers were involved in crashes due to another driver's error. These drivers are exposed to some type of injury or risk due to drivers violating traffic rules. Of the 329,037 crashes (records of drivers violating a traffic rule in 2-vehicle crashes), records pertaining to traffic violations not listed in Table 1 were removed from the data set. The final data set to assess risk to other drivers due to violation of a traffic rule consisted of 139,505 records. Figure A-1 (see online supplement) summarizes the data processing and development of the final database for the analysis. The dependent variable in this study is driver injury severity. HSIS defines 5 levels of injury severity: fatality, incapacitating injury, nonincapacitating injury, possible injury, and property damage only (PDO). Incapacitating injury means that the person was impaired or disabled because of the crash. Nonincapacitating injury is any injury other than a fatal injury or an incapacitating injury that is evident to observers at the scene of the crash. Possible injury requires very minimal medical assistance, and no injury is observed in case of PDO crash. In this study, the severity of driver injury was redefined into 3 categories. Fatal and incapacitating injury levels were combined and considered the severe injury category, and nonincapacitating injury and possible injury levels were combined and considered the moderate injury category. PDO is considered the no injury category.
Multinomial logit models were developed to examine the effect of different traffic violations (independent variable) on driver injury severity (dependent variable). Unarguably, factors such as age and gender of the driver, lighting condition, and other network characteristics have an effect on the number of crashes (possibly, injury severity). However, the intent is not to have such independent variables control the role of traffic violation on risk. Furthermore, penalty points and fine amount do not generally vary with these independent variables. Therefore, these variables were not considered as independent variables in this study.
Two different sets of multinomial logit models were developed to examine (1) the risk drivers pose to themselves by violating traffic rules and (2) the risk drivers violating traffic rules pose to other drivers. The maximum likelihood estimate was used in estimating the coefficients of the variables. The coefficients of the model explain whether the independent variable increases or decreases the probability of the dependent event. To explain the extent of the effect of the independent variables on occurrence of the dependent variable, the odds ratio concept was used. The odds ratios also indicate the probability value. Therefore, only the estimated odds ratios and confidence limits from the developed models are presented in this article. Ben- Akiva and Lerman (1985) presented an in-depth discussion and details pertaining to the development of multinomial logit models and the application of the odds ratio concept.
In logistic regression models, the reference variable should be defined so that the odds can be estimated. Disregarded traffic signal is considered as the reference for the independent variable; that is, traffic violations. The 2 different cases for which models were developed are as follows: 1. Risk drivers pose to themselves by violating traffic rules when compared to risk drivers pose to themselves by disregarding traffic signals. 2. Risk drivers violating traffic rules pose to other drivers when compared to risk drivers disregarding a traffic signal pose to other drivers. In all of the models developed, the dependent reference variable is PDO.
Overall, 227,504 records from single-, 2-and 3-vehicle crashes were used to develop the first set of models (risk to themselves; Table 2), and 139,505 records from 2-vehicle crashes were used to develop the second set of models (risk to other drivers; Table 3). Table 1 shows the frequency of selected traffic violations and the percentage distribution of injuries among those violations. The results presented in Table 1 were developed before preparing the data for modeling and analysis. About 51% of the total drivers involved in crashes committed some kind of traffic violation, whereas ∼74% of the severe driver injuries occurred due to some kind of traffic violation during the study period. Driving under the influence of alcohol and going the wrong way each contributed ∼10% of severe driver injuries from 2010 to 2013. The frequency of driving under the influence of alcohol and going the wrong way is less compared to their contribution to severe driver injuries. Exceeding the authorized speed limit and exceeding the safe speed limit for conditions each contributed ∼8% of the total severe driver injuries. Drivers less frequently exceed authorized speed limit when compared to exceeding the safe speed limit for conditions. Among the considered traffic violations, failing to yield the right of way followed by exceeding the safe speed limit for conditions contributed most toward moderate severe driver injuries and PDO crashes. Failing to yield the right of way also contributed considerably toward severe driver injuries. Operating a vehicle erratically or aggressively also had a significant contribution toward severe driver injuries. The percentage of drivers involved in crashes due to improper lane changes is higher than the percentage of drivers involved in crashes due to disregarding traffic signals. However, the contribution of disregarding traffic signals to severe driver injuries is slightly greater than that for improper lane change. Disregarding a stop sign contributed to around 2% of severe driver injuries. The individual contribution of traffic violations such as disregarded a yield sign, right turn on red, passed on a hill, passed on a curve, and improper or no signal to total crashes during the study period was less than 0.1%.

Results
Several factors could encourage drivers to violate traffic rules. Figure 1 exhibits a comparison of the percentage of crashes due to and not due to traffic violations under different lighting conditions and by gender. During daylight, the percentage of crashes due to violation of traffic rules is very high when compared to the percentage of crashes due to no traffic violation. Figure 1 implies that drivers are more likely to not comply with traffic rules when they have good visibility of the roadway. The difference between the percentage of crashes due to and not due to traffic violations is lower during dawn and dark, unlighted roadway conditions. Male drivers are more likely to violate traffic rules and be involved in a crash compared to female drivers; similar to observations made by Moyano-Diaz (1997), Yagil (1998), andGonzalez-Iglesias et al. (2012). This shows that female drivers are more likely to follow traffic rules compared to male drivers. In simple terms, male drivers are relatively more aggressive and risk takers.  Figure 2 shows the percentage of drivers violating traffic rules by age group. From Figure 2, the percentage of drivers violating traffic rules within an age group reduced with an increase in the age of drivers up to some extent and then decreased again. Drivers less than 26 years of age had a huge difference in the percentage (% drivers violating traffic rules minus % drivers involved in crashes without violating any traffic rule). However, this difference is lowest for middle-aged drivers and increased for drivers older than 65 years, who often commit violations due to poor vision (Kline et al. 1992) and misjudgment of gaps. Drivers younger than 18 years have a high percentage of traffic violations, implying that young drivers take more risks and that their perception of risk is different than that of adult and older drivers. Table 2 depicts different traffic violations and their likelihood of resulting in severe injury and moderate injury to the driver compared to disregarding traffic signal. In Table 2, 95% Wald confidence limits are provided for the odds ratios. These values explain the range of odds ratio value at a 95% confidence level. If a driver disregarded a yield sign, he or she was equally likely to succumb to severe injury as if he or she disregarded a traffic signal. Disregarding a yield sign is less likely to result in moderate driver injury compared to driver injury in disregarding traffic signals. Disregarding a stop sign is around 6 times more likely to result in severe driver injury and 1.4 times more likely to result in moderate driver injury. Disregarding other traffic signs is 2 times more likely to result in severe driver injury and less likely to result in moderate driver injury. However, the probability estimate for moderate driver injury is not statistically significant in the case of disregarding other traffic signs and disregarding road markings. Exceeding the authorized speed limit is almost 40 times more likely to result in severe driver injury compared to disregarding traffic signals and is 3 times more likely to result in moderate driver injury. Exceeding the safe speed limit for conditions is equally likely as disregarding traffic signals to result in moderate driver injury and 3 times more likely to result in severe driver injury. Improper turning is less likely to result in severe and moderate driver injuries compared to disregarding traffic signals. Right turn on red is equally likely as disregarding traffic signals to result in severe driver injury but is not significant at a 95% confidence level. Among all of the traffic violations, right turn on the red is least likely to result in moderate driver injury. This implies that most right turn on red crashes result in PDO. Going the wrong way is the second highest traffic violation in terms of the probability of resulting in severe driver injury. Improper lane change is more likely to result in PDO, and improper lane use is more likely to result in severe driver injury and less likely to result in moderate driver injury. Driving under the influence of alcohol or drugs, operating defective equipment, aggressive driving, passing on a curve, improper passing, improper lane use, and going the wrong way are more likely to result in severe driver injury. Improper turning, improper lane change, and following a vehicle closely are less likely to result in severe driver injury compared to disregarding traffic signals. Failing to yield the right of way, passing on a hill, and improper or no signal are not significant at a 95% confidence level and are less likely to result in severe driver injury. All other traffic violations except operating a vehicle aggressively, driving under the influence of alcohol or drugs, going the wrong way, and disregarding a stop sign are less likely to result in moderate driver injury. Overall, 16 out of 21 considered traffic violations are more likely to result in severe driver injury to the driver compared to disregarding a traffic signal and 7 out of 21 considered traffic violations are more likely to result in moderate driver injury to the driver compared to disregarding a traffic signal.

Risk of violating traffic rules to other drivers
Traffic violators not only put themselves at risk but also put other drivers at risk. Therefore, this part of the study focused on risk drivers violating traffic rules pose to other drivers. Table 3 summarizes the risk to other drivers due to a driver violating a traffic rule. Odds ratios as well as 95% Wald confidence limits for odds ratios are shown in the table.
The risk drivers pose to themselves is higher than the risk drivers violating traffic rules pose to other drivers. Exceeding the authorized speed limit poses the highest risk to other drivers.
Going the wrong way is more likely to put other drivers at risk among all traffic violations. Driving under the influence of alcohol or drugs is almost 5 times more likely to put other drivers at higher risk (severe injury). Disregarded a yield sign, disregarding other road signs, disregarding road markings, improper turning, right turn on red, improper lane change, improper lane use, passing on a hill, passing on a curve, other improper passing, failing to yield the right of way, improper or no signal, following a vehicle closely, and operating defective equipment are less likely to result in severe injury to other drivers when compared to disregarding a traffic signal.
The likelihood of severe injury to oneself when driving under the influence of alcohol and drugs is 15 and 7, respectively. However, the likelihood of severe injury to other drivers due to driving under the influence of alcohol and drugs is 4.6 and 5.3, respectively. Likewise, other drivers are exposed to slightly higher moderate injury due to driving under the influence of drugs than due to driving under the influence of alcohol. Disregarding a stop sign is more likely to put other drivers at severe risk compared to disregarding a traffic signal. Among the considered traffic violations, 7 are more likely to put other drivers at risk compared to disregarded traffic signals. Except for aggressive driving, all other traffic violations are more likely to result in moderate injury to other drivers. Overall, other drivers are unintentionally involved in crashes and are at risk because of traffic violators.

Discussion
A majority of the traffic violations have higher probabilities of resulting in severe driver injury compared to injury when disregarding traffic signals. Exceeding the speed limit is more likely to result in severe injury to the driver compared to driver injury due to disregarding traffic signals. However, going the wrong way is more likely to result in severe injury to other drivers compared to any other traffic violation. Driving under the influence of alcohol is 2 times more likely to result in severe injury to the driver than driving under the influence of drugs. The associated risk varies by the type of traffic violation. The risk drivers violating traffic rules pose to themselves is higher than the risk they pose to other drivers.
The findings from this study serve as documented evidence to educate and create awareness among drivers of the risk of violating traffic rules for themselves as well as for other drivers. Educating drivers about the risk associated with various traffic violations could help them develop safe driving behaviors, which would eventually improve safety on roads and contribute toward reaching the "zero traffic deaths" vision. The findings from this study could also be used by policy makers and transportation system managers to identify traffic violations that need to be immediately addressed to reduce both crashes and fatalities.
Traffic rule violators increase the risk to other road users. Penalty points may be imposed on their driver's license, they may have to pay a fine, and they may have their license revoked depending upon the type of violation committed (whether the traffic violation leads to a crash or not). It is important to present the risk in terms of injury severity to themselves as well as other drivers (road users, in general) to define penalty points or fine amount. The findings from this research provide vital insights to integrate potential risk and validate or revise enforcement penalties (points and fine).
In this study, only drivers involved in crashes were taken into consideration for analysis and modeling. Subject to availability of quality data, the study could be extended to examine the effect of violating a traffic rule on passengers, pedestrians, and bicyclists.
Likewise, only the primary contributing factor or traffic violation was taken into consideration for analysis and modeling in this research. Certain combinations of multiple violations may increase risk to drivers and other road users. The risk could vary by gender, age group, lighting condition, and network characteristics. The effect of combinations of violations on risk to drivers and other road users by gender, age group, or other characteristics merits research and investigation in the future.
Drivers often perceive that violating a traffic rule does not lead to a crash or severe crash. However, the reality may be far different than what drivers often perceive. There is also a need to compare risk due to violating traffic rules by age and gender with risk perceptions by age and gender to identify and educate target groups whose perceptions substantially differ from the reality.