- About this Journal
- Abstracting and Indexing
- Aims and Scope
- Annual Issues
- Article Processing Charges
- Articles in Press
- Author Guidelines
- Bibliographic Information
- Citations to this Journal
- Contact Information
- Editorial Board
- Editorial Workflow
- Free eTOC Alerts
- Publication Ethics
- Reviewers Acknowledgment
- Submit a Manuscript
- Subscription Information
- Table of Contents
Advances in Mechanical Engineering
Volume 2013 (2013), Article ID 905075, 8 pages
The Influence of Contributory Factors on Driving Violations at Intersections: An Exploratory Analysis
1Department of Traffic Engineering, Harbin Institute of Technology, Harbin 150090, China
2Department of Industrial and Systems Engineering, University of Washington, Seattle, WA 98195, USA
3Department of Traffic Engineering, Northeast Forestry University, Harbin 150040, China
Received 19 September 2013; Accepted 14 October 2013
Academic Editor: Fenyuan Wang
Copyright © 2013 Chuanyun Fu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Driving violation has been identified as one of the prominent contributing factors involved in crashes occurring at intersections. However, very little literature has studied the influence of contributory factors on driving violation. The goal of the present study was to analyze the effect of contributory factors on traffic sign and signals (TSS) violation, turning-yielding-signaling (TYS) violation, and speeding related (SR) violation at intersections, and the likelihood of each type of driving violation was predicted by using a multinomial logistic model. The findings of this study indicated that younger drivers (ages 15–24) had a propensity to commit SR violations, but older drivers (ages 65+) had the adverse situations. The likelihood of SR violations for males was higher than TYS violations, while contrary outcome was obtained during daylight. At traffic sign or signals controlled intersections, the odds of TSS violations and TYS violations were greater than SR violations, and similar outcomes were gained in four-way intersections or T-intersections. Drivers distracted cognitively were more likely to be in commission of TYS violations than SR violations, whereas it is opposite to passenger or in-vehicle related distractions. Inattention/distraction significantly affected the likelihood of each type of driving violation. Implications of the findings from current study are discussed.
Intersection is the conjunction node of the whole road network, and traffic flows from different directions converge there. Drivers have to spare their attention from the main driving tasks to other relevant things (e.g., traffic sign or signals, crossing vehicles and pedestrians, etc.) due to the dynamic and complex situation at intersections . Potential conflicts and crashes between road users are more likely to occur . In the United States, there were 6,898 fatalities resulting from motor vehicle crashes at intersections in 2011 , and 1,126 estimated fatalities happened at the signalized intersections and 1,492 estimated fatalities at the stop/yield controlled intersections . Driving violation, as one type of driving errors , has been identified as a prominent factor involved in the crashes occurring at intersections .
The three most common types of violation that impair the driving performance for drivers are turning-yielding-signaling (TYS) violation, traffic sign and signals (TSS) violation, and speed related (SR) violation . TYS violations usually occur when drivers fail to properly turn, or fail to signal for turn or stop, and not yield the right of way to other vehicles. TSS violations usually involve committing stop sign running and failure to obey the rules of traffic signals. SR violations, in other words, are that vehicles are travelling beyond the posted speed limits. These definitions indicate the difference between violations that are important when considering what factors should probably be involved in.
Red light running (RLR) at signalized intersections, as one category of TSS violations, has been widely studied over the past two decades. Red light running had been estimated to result in approximately 260,000 crashes each year, with 750 fatal accidents . In the urban setting, about 5% crashes are caused by red light runners . According to a field investigation analysis, red light runners who were younger people were less likely to wear seat belts compared to the drivers who did not run red lights . A similar conclusion was drawn from comparing characteristics of crash-involved red light runners with nonviolation drivers . Male drivers are more likely to commit red light violations than females [9, 10]. However, red light runners’ characteristics are found to be insignificant indicators for these violation rates after turning off red light cameras . The influence of geometric design factors and traffic characteristics at intersections on RLR was investigated, and several regression models were proposed to predict the RLR frequency .
Unlike traffic signal, stop sign which is used to notify drivers to stop their vehicles completely before entering the intersection is the primary traffic control device in USA . There is few literature about stop sign violation at intersections. Pietrucha et al.  concluded that 71% of the chronic stop sign violators had no awareness of the risks in running these signs. Chovan et al.  conducted a detailed analysis of 100 right-angle collisions and concluded that 42% crashes occurred due to failure to stop at two-way stop sign controlled intersections. Retting et al.  draw a conclusion that drivers younger than 18 and drivers aged 65+ were more likely to run through stop signs than other age groups. Romano et al.  found that race/ethnicity appeared to be an important reason for fatal stop sign running crashes. Braitman et al.  formed a conclusion that failure to yield to right-of-way was the contributing factor leading to majority of older drivers’ crashes at the intersections controlled by stop signs. Keay et al.  uncovered the differences in older drivers’ failure to stop at stop signs under the urban and rural environments.
Speeding is always considered as the most frequent violation . It is found that the speeding violator always thinks himself/herself doing that within his/her rights . Exceeding the posted speed limit is demonstrated to be an outcome of drivers’ accumulating speeding habits . Excessive speeding is considered to be a minor violation , but the crash rate due to excessive speeding has shown to be much higher than exceeding speed limit . One study assessed that 50% of the vehicles on the road were violating speed limit in France . Nallet et al.  concluded that frequently or fairly frequently exceeding speed limit by 10 km/h distinctly increased crash occurrence rate, and most of these violators did not realize the potential danger. Research by Liu  had shown that traffic light status was the highest contributor to speeding at intersections by examining and comparing with site, rush-hour status, vehicle type, and driver gender.
However, very little is known from these studies about the effect of different factors on the propensity to commit one of driving violations at intersections. Furthermore, there are lots of traffic conflicts and crashes occurring at intersections due to improperly turning or yielding, but only little literature has studied the TYS violations.
Therefore, given that driving violations are overrepresented in terms of crash cause at intersections, the objective of this study is to analyze how the contributory factors impact driving violations. Understanding this will help us to understand the influence of different factors on traffic safety and traffic operation efficiency at intersections. This exploratory analysis is based on a hypothesis that the contributory factors will affect the likelihood of committing a certain type of driving violations for drivers. The current paper proceeds with a description of the method applied for this study (multinomial logistic regression), then the results are reported and discussed, and conclusions and recommendations for future research are presented.
Data employed for this study is from the National Automotive Sampling System (NASS) General Estimates System (GES) from the year 2011. This GES data is available at (ftp://ftp.nhtsa.dot.gov/GES/), which is from the National Highway Traffic Safety Administration (NHTSA). The data is a probability sample selected through three stages of police-reported traffic crashes for estimating the national crash tendency by weighting. The data comprises detailed descriptions of crash characteristics, particular demographics of occupants involved, features of crash vehicles, road environment where crash occurred (including traffic management infrastructure, etc.), specific violation types for drivers, and the distracted state of drivers in the crashes. There are five files including all this information and they are merged into one big file via R studio version 0.97.248. Only driving violations that occurred at intersections were included in this study. Data associated with nonmotor vehicle (e.g., motorcycle, bicycle, and moped) was removed. Furthermore, the missing (not reported) and unknown data of the important variables were also deleted.
2.2. Driving Violation Types Classification
The number of each violation type is presented in Figure 1. The three major violation types applied for present study are TYS violations, TSS violations, and SR violations, which represent 41.3%, 23.1%, and 14.8%, as calculated from all the moving violations that impair driving performance, respectively. These three driving violation categories account for approximately 80% of the moving violations which drivers committed at intersections. Therefore, they are employed as the categories for the response variable of the multinomial logistic regression model.
2.3. Independent Variables
Gender and age [25, 26] have previously been identified as the influence factors for driving violation in many studies. Males are more likely to commit driving violation than females [24, 26]. Gender was therefore treated as a contributory factor in our analysis. Age of drivers was categorized into three groups: young driver (ages 15–24), middle age group (ages 25–65), and old driver (ages 65+). Poor road surface and light conditions have been shown to influence driving performance and injury severity of drivers at intersections . In our study, we had two levels in road surface conditions (dry pavement or poor surface condition) and two lighting levels: daylight or nondaylight. Traffic signals (i.e., traffic light status and signal warning flasher) have been confirmed to be the contributor to speeding and red light running violation [24, 28]. Together with traffic signals, stop sign were regarded as the explanatory factors, because the poor visibility of stop sign due to traffic condition (e.g., percentage of large size vehicle) makes the driver more likely to fail to stop [18, 29]. In many cases, drivers fail to yield right of way to other road users owing to inadequate attention allocation under the complex intersection situations . These complex traffic environment and geometric sizes are different according to the types of intersection. There were only a few data points in the last two types of intersections. Hence, we only included four-way and T- and Y-intersections in this variable.
Driver distraction that is defined as anything not related to driving distracting a driver from the primary task of driving can be categorized into cognitive distraction, cell phone related distraction, passenger related distraction, and in-vehicle related distraction . Many studies showed that distraction, as a causal factor, contributed to a range of errors occurring at intersections including missing the stop/yield sign or traffic signal, misjudging the timing of yellow lights, and failure to notice other oncoming vehicles . In this study, the unknown specific distraction type or inattention was included as a variable as well as other four distraction types. In the light of the findings [18, 24], urban and rural environments play an important role in driving violation, especially for older drivers. Hence, both were also considered in this analysis with the urban area classified as an area with a population greater than 50,000 people. All these observed variables were treated as the binary indicators (e.g., 1 denotes the violation which occurred in the urban settings, 0 denotes not).
2.4. Analysis Based on a Multinomial Logistic Model
A multinomial logistic model was applied for predicting the probabilities of the different possible outcomes of the discrete unordered categories of response (dependent) variable based on the same categorical observed independent variables. The variance and significance of the coefficients for this regression model were estimated through the “VGAM” package in R studio version 0.97.248. Significance was assessed at . The outcomes from this model are used to predict the likelihood that the response variable will be in one category (violation type) as compared to another. Therefore, in this study, there are three pair contrasts for each violation type: TSS violation versus SR violation, TYS violation versus SR violation, and TYS violation versus TSS violation. The output of this regression model typically uncovers all but one of the relationship contrasts and provides more insights to this topic.
There were 5,679 (unweighted) driving violations included in this analysis. Male drivers were involved in 53% of driving violations () and female in about 47% (). Young drivers (ages 15–24) were engaged in 30% of driving violations () and old drivers (ages 65+) in approximately 13% (). About 44% of driving violations () occurred in the urban settings, and 17% of driving violations () took place in rural environment. Over 76% of driving violations () occurred during the daylight condition and 83% of driving violations () occurred on the clean pavement. More than half of driving violations () happened at signalized intersections and about 1,896 happened at the stop/yield controlled intersections. A majority of driving violations () occurred when drivers paid full attention to driving without any distraction. Nearly 5% of driving violations () took place when drivers were distracted cognitively, and approximately 10% of driving violations () occurred when drivers were in inattention status. The proportions of driving violations that happened when drivers were distracted by other distraction categories (passenger related distraction (), cell phone distraction (), in-vehicle related distraction (), and distraction from outside of vehicles ()) are less than 1%. Table 1 indicates the number of driving violations for drivers by violation and intersection types. Approximately 98% driving violations occurred at the four-way intersections () and T-intersections ().
Contrasts between each violation category were uncovered through the multinomial logistic model, and the outcomes were shown in Table 2. The model was adjusted for certain variables identified in the previous section as having a significant influence on violations. There were no significant differences in driving violation category based on two types of distraction (i.e., cell phone related distraction and distraction from outside of vehicle) or on two categories of intersection (i.e., Y-intersection and five-point or more). Young drivers (ages 15–24) were more likely to be involved in SR violations than TSS violations () and TYS violations (); they were more likely to commit TSS violations () as compared to SR violations when they were in the state of inattention. Compared to SR violations () and TSS violations (), young drivers (ages 15–24) tended to more likely commit TYS violations at the signalized intersection. However, older drivers (ages 65+) were 2.12 times more likely to commit TSS violations and 2.92 times more likely to commit TYS violations when compared to SR violations. Moreover, older drivers (ages 65+) were 1.38 times more likely to be involved in TYS violations than TSS violations.
Male drivers were less likely to be involved in TYS violations than SR violations (), and while driving during daylight they were more likely to commit TYS violations () as compared to SR violations. During daylight, drivers were 1.56 times more likely to be engaged in SR violations when compared to TYS violations. If driving on the poor road surface, drivers were more likely to be involved in SR violations as compared to TSS violations () and TYS violations (). Compared to SR violations, drivers were 373.37 times more likely to commit TSS violations and 2.91 times more likely to commit TYS violations at traffic signals controlled intersections, and they were also more likely to be engaged in TSS violations when compared to TYS violations. The similar outcome was observed at the stop/yield sign controlled intersections.
If the driver was cognitively distracted at an intersection, he or she was more likely to be involved in TYS violation than SR violation (). And the likelihood of committing TSS violation () was much higher than that of TYS violation. When driver was distracted by his/her passengers at an intersection, he/she was 3.70 times more likely to commit SR violation than TYS violation. When drivers were distracted by in-vehicle related distraction, they were 4.76 times more likely to be involved in SR violations and 4.76 times more likely to be involved in TSS violations as compared to TYS violations. If drivers were in the state of inattention, they were more likely to be engaged in SR violations than TSS violations () and TYS violations (). Compared to SR violations, inattention drivers were 2.39 times more likely to be involved in TSS violations and 1.65 times more likely to commit TYS violations at an urban intersection, whereas inattention drivers were more likely to be involved in TYS violations () than TSS violations at the signalized intersections.
In the urban settings, drivers were more likely to be engaged in TSS violations () and TYS violations () when compared to SR violations. At the four-way intersection, drivers were more likely to be involved in TSS violations () and TYS violations () than SR violations, and they were also 3.03 times more likely to be engaged in TSS violations than TYS violations. Nevertheless, drivers were more likely to be involved in SR violations when compared to TSS violations () and TYS violations () at the signals controlled four-way intersection. Furthermore, they were 4.20 times more likely to commit TYS violations than TSS violations under the same situation. At the T-intersection, the likelihood of being involved in TSS violations () or TYS violations () was higher than SR violations. However, drivers were 9.09 times more likely to be engaged in SR violations when compared to TSS violations at the signals controlled T-intersection.
The three categories of driving violations in this study include the majority of driving violations by drivers, and then insights about the differences between younger and older age groups being involved in driving violation at intersections can be gained. Younger drivers (ages 15–24) were more likely to be involved in SR violations than TSS violations or TYS violations, while older drivers (ages 65+) were more likely to commit TSS violations and TYS violations as compared to SR violations.
Younger drivers having a propensity to speeding is in line with other studies , which reported that young drivers were overrepresented in drivers travelling higher speed. Cooper  reported that there was a total distinction between the conviction categories of exceeding the posted speed limit and excessive speed in terms of accident-violation relationship. Nallet et al.  further confirmed that exceeding the speed limit by 10 km/h increased crash occurrence by a factor of 1.4.
In addition, the inattention younger drivers who shifted their attention from the task of driving to unknown distraction could not promptly notice the traffic lights or presence of stop/yield signs or the oncoming conflicting vehicles. Therefore, compared to SR violation, this inattention younger group was more likely to commit TSS violation. Interestingly, younger drivers were more likely to be involved in TYS violations than SR violations or TSS violations at the signalized intersections. Two researches showed that young drivers always overestimated their ability to handle multitask  and had higher propensity for risk-taking . Our hypothesis is that younger drivers would pay less attention to detailed traffic safety principles at the signalized intersections due to reasons discussed above.
Older drivers (ages 65+) were found to have less likely SR violations than TSS violations and TYS violations at intersections. They are more conservative when crossing intersection , with slower speed, and are able to come to a more accurate stop at the intersections than other age groups . However, older drivers would have lower speed when crossing intersections so that all-red phase usually occurs before they fully pass the intersections . This conclusion goes along with the current study. Nevertheless, one observational study suggested  that older drivers were more likely to stop at red traffic lights than younger and middle age groups. This conclusion may further hint that older drivers tend to have more TYS violations than TSS violations. There is a relationship between psychological and physical ability loss with aging and crash risk increase , which can help explain older drivers’ failure to stop at stop signs or red light running due to poor vision or cognition.
Male drivers are reported to have more crashes and are more likely to commit driving violations [26, 38]. Compared to TYS violations, males were more likely to be involved in SR violations at intersections. That is similar to the conclusion of Liu’s study  that male drivers aged under 55 years old had the most speeding propensity at an intersection among all age groups. However, during the daylight, on the contrary, male drivers were shown to be more likely to commit TYS violations than SR violations. Drivers either under the daylight condition or driving on poor pavement were all more likely to be involved in SR violations than TYS violations at intersections. According to this current study, policy makers should consider different characteristics of different drivers (teenagers, older people, and males/females) when designing traffic systems as well as training programs to help reduce potential crashes. In order to do this, further studies might be needed to study characteristics as well as psychologies of different groups of drivers.
At signalized intersections, approaching drivers may encounter a dilemma zone when traffic signal changes from green to yellow. If they are too far away from the stop line they might speed up to go straight through intersections which result in red light running (or exceeding the speed limit). On the other hand, they might abruptly stop even though they can safely cross the intersection. In this circumstance, it may cause rear-end crashes if the following drivers make a conflicting decision . In addition, a driving simulator experiment conducted by Harb et al.  demonstrated that larger size vehicles at the approaches of signalized intersection leaded to more red light running events due to vertical visual blockage. These previous observations corroborate our findings that drivers were more likely to be involved in TSS violations than other two categories of violations at the traffic signal controlled intersections. However, drivers driving at signalized four-way intersections or signal controlled T-intersections were all more likely to commit SR violations as compared to TSS violations. A similar conclusion was found in Liu’s study  at one signalized urban four-way intersection and one suburban intersection that traffic light status was the most significant contributor to speeding.
At approaches to intersection controlled by stop sign, there are several factors that may raise the risks of crashes: failure to wait for sufficient gaps, failure to detect potential conflicts, and paying less cautions before entering . A simulation study  reported that stop sign running in rural area might be more common, because there were less traffic and better visibility. The above mentioned researches had confirmed the finding of current study that drivers tended to be more likely to commit TSS violations in rural area and stop sign running may lead to severe crash risks. In addition, there were no statistical interactions between stop sign and four-way intersection or T-intersection, and the similar outcomes of these two types of intersection can be gained from the model.
Driving violations may be affected by different types of distractions . This study provided more insights about the effect of how driver distractions influence driving violation. As compared to SR violations, drivers distracted cognitively were more likely to commit TYS violations; while drivers distracted by their passengers or in-vehicle related distraction were less likely to commit TYS violations. When compared to TSS violations, the outcomes for cognitive distraction and in-vehicle were opposite with each other. And, furthermore, inattention drivers were more likely to be involved in SR violations than TSS violations and TYS violations, and a totally inverse result can be obtained in the urban settings. Nonetheless, inattention drivers were more likely to commit TYS violations than TSS violations at signalized intersections. Differences in the influence of each type of distraction on the three driving violations can be considered in the design and improvement of intersection crossing assist system for further enhancing the safety level of intersections.
However, the effect of passenger distraction on driving violation should be interpreted with caution. One observation study  suggested that drivers driving alone at intersections were more likely to commit red light running and were typically in a hurry situation. In other words, they were less likely to have driving violation when crossing intersections with passengers. We can explain this phenomenon that drivers would have to take more responsibilities for their passengers which require them to drive with more caution. But teen drivers driving with young male passengers have higher risks involved in crashes . Further studies might be needed to examine this phenomenon. Several reasons might be associated, such as the purposes of the trip (going for parties, drunk, etc.) and characteristics of male teenagers (for instance, more energetic but negligent).
In this study, cell phone related distraction did not include texting and sending/receiving/reading text. Experimental and naturalistic driving studies showed that text messaging would negatively influence driving performance [42, 43]. Driving, as a complex, multitasks activity, needs high demand on attention, especially visual attention . Text messaging is shown to be a predominantly visual activity . Further studies could be focused on whether and how texting would impact driver performance, especially when crossing intersections.
Driving violations at intersections have long been an extensive topic in the domain of traffic safety. If the influence of contributory factors on types of driving violations is well-understood, countermeasures can be proposed around a variety of factors for enhancing the safety level at intersections. In the present study, drivers’ age and gender, light condition, road surface condition, traffic signals, stop/yield sign, driver distraction, inattention, the urban settings, and types of intersection (road/geometric characteristics) are considered through significantly testing as variables for predicting the odds of each type of driving violation (turning-yielding-signaling violation, traffic sign and signals violation, and speeding related violation) and then uncovering the effect of each contributory factor on three most common driving violations based on a multinomial logistic model.
There are a few limitations of current study. First of all, the data from GES cannot reveal all the aspects of driving violations. On one hand, GES database is a probability sample of police accident reports. Since it was a self-reported study, when asked about the crashes some drivers tended to hide the truth to avoid taking responsibilities. On the other hand, GES database does not include the data related to text messaging. Furthermore, the differences between red light running and stop sign running are not explored in the present research. However, this research provides more insights into the influence of contributory factors on driving violations at intersections, especially the different effects of driver distractions on driving violations.
In terms of future study, an examination of the interaction and relationship between types of driving violations could potentially be a fruitful research scope. Instead of sole measures, multicountermeasures would be a new development orientation. In addition, the topic explored in current study needs further in-depth research based on experimental and naturalistic driving data.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
This study conducted at the Lab of Human Factors and Statistical Modeling (HFSM), University of Washington, is sponsored by China Scholarship Council (CSC) and a grant from National Science Foundation of China (project no. 51078113).
- J. Werneke and M. Vollrath, “How to present collision warnings at intersections? A comparison of different approaches,” Accident Analysis and Prevention, vol. 52, pp. 91–99, 2013.
- K. L. Young, P. M. Salmon, and M. G. Lenné, “At the cross-roads: an on-road examination of driving errors at intersections,” Accident Analysis and Prevention, vol. 58, pp. 226–234, 2013.
- NHTSA, Traffic Safety Facts: 2011 Motor Vehicle Crashes: Overview, NHTSA, Washington, DC, USA, 2012.
- NHTSA, General Estimates System, 2011, ftp://ftp.nhtsa.dot.gov/GES/GES11/.
- N. A. Stanton and P. M. Salmon, “Human error taxonomies applied to driving: a generic driver error taxonomy and its implications for intelligent transport systems,” Safety Science, vol. 47, no. 2, pp. 227–237, 2009.
- R. A. Retting, J. F. Chapline, and A. F. Williams, “Changes in crash risk following re-timing of traffic signal change intervals,” Accident Analysis and Prevention, vol. 34, no. 2, pp. 215–220, 2002.
- J. S. Milazzo, J. E. Hummer, and L. M. Prothe, “A recommended policy of automated electronic traffic enforcement of red light running violations in North Carolina,” Final Report, Institute for Transportation Research and Education, North Carolina State University, Raleigh, NC, USA, 2001.
- R. A. Retting and A. F. Williams, “Characteristics of red light violators: results of a field investigation,” Journal of Safety Research, vol. 27, no. 1, pp. 9–15, 1996.
- S. D. Lawson, Red-Light Running: Accidents and Surveillance Cameras (aa/bcc-3), AA Foundation for Road Safety Research and Birmingham City Council, Basingstoke, UK, 1991.
- T. Rosenbloom, A. Ben-Eliyahu, D. Nemrodov, A. Biegel, and A. Perlman, “Committing driving violations: an observational study comparing city, town and village,” Journal of Safety Research, vol. 40, no. 3, pp. 215–219, 2009.
- B. E. Porter, K. L. Johnson, and J. F. Bland, “Turning off the cameras: red light running characteristics and rates after photo enforcement legislation expired,” Accident Analysis and Prevention, vol. 50, pp. 1104–1111, 2013.
- S. E. Hill and J. K. Lindly, Red Light Running Prediction and Analysis, The University of Alabama, Tuscaloosa, Ala, USA, 2003.
- R. A. Retting, H. B. Weinstein, and M. G. Solomon, “Analysis of motor-vehicle crashes at stop signs in four U.S. cities,” Journal of Safety Research, vol. 34, no. 5, pp. 485–489, 2003.
- M. T. Pietrucha, K. S. Opiela, R. L. Knoblauch, and K. L. Crigler, Motorist Compliance with Standard Traffic Control Devices, U.S. Department of Commerce, Springfield, Va, USA, 1989.
- J. D. Chovan, L. Tijerina, D. L. Pierowicz, and D. L. Hendricks, “Examination of unsignalized intersection, straight crossing path crashes and potential ivhs countermeasures,” Tech. Rep. Dot-vntsc-nhtsa-94-2, hs-808-152, U.S. Department of Transportation, Washington, DC, USA, 1994.
- E. Romano, R. Voas, and S. Tippetts, “Stop sign violations: the role of race and ethnicity on fatal crashes,” Journal of Safety Research, vol. 37, no. 1, pp. 1–7, 2006.
- K. A. Braitman, B. B. Kirley, S. Ferguson, and N. K. Chaudhary, “Factors leading to older drivers' intersection crashes,” Traffic Injury Prevention, vol. 8, no. 3, pp. 267–274, 2007.
- L. Keay, S. Jasti, B. Munoz et al., “Urban and rural differences in older drivers' failure to stop at stop signs,” Accident Analysis and Prevention, vol. 41, no. 5, pp. 995–1000, 2009.
- N. Nallet, M. Bernard, and M. Chiron, “Self-reported road traffic violations in France and how they have changed since 1983,” Accident Analysis and Prevention, vol. 42, no. 4, pp. 1302–1309, 2010.
- S. Gaymard, Ceux qui transgressent les règles pensent être dans leur droit: lettre d'into sécurité routière no 27 septembre, 2006.
- P. J. Cooper, “The relationship between speeding behaviour (as measured by violation convictions) and crash involvement,” Journal of Safety Research, vol. 28, no. 2, pp. 83–95, 1997.
- F. Simon and C. Corbett, “A small roadside study of drivers caught breaking speed limits,” in Behavioural Research in Road Safety II: Proceedings of a Seminar at Manchester University Crowthorne, Transport and Road Research Laboratory, Berkshire, UK, 1992.
- C. Pérez-Diaz, “Risk of driving: male aggressiveness, violence, and delinquency,” Revue d'Epidemiologie et de Sante Publique, vol. 52, no. 4, pp. 368–375, 2004.
- B.-S. Liu, “Association of intersection approach speed with driver characteristics, vehicle type and traffic conditions comparing urban and suburban areas,” Accident Analysis and Prevention, vol. 39, no. 2, pp. 216–223, 2007.
- E. Becic, M. Manser, C. Drucker, and M. Donath, “Aging and the impact of distraction on an intersection crossing assist system,” in Accident Analysis and Prevention, vol. 50, pp. 968–974, 2013.
- D. Yagil, “Gender and age-related differences in attitudes toward traffic laws and traffic violations,” Transportation Research F, vol. 1, no. 2, pp. 123–135, 1998.
- A. Habibovic and J. Davidsson, “Requirements of a system to reduce car-to-vulnerable road user crashes in urban intersections,” Accident Analysis and Prevention, vol. 43, no. 4, pp. 1570–1580, 2011.
- F. Galatioto, T. Giuffrè, M. Bell, G. Tesoriere, and T. Campisi, “Traffic microsimulation model to predict variability of red-light running influenced by traffic light operations in urban area,” Procedia—Social and Behavioral Sciences, vol. 53, pp. 872–880, 2012.
- R. Harb, E. Radwan, and X. Yan, “Larger size vehicles (LSVs) contribution to red light running, based on a driving simulator experiment,” Transportation Research F, vol. 10, no. 3, pp. 229–241, 2007.
- J. Werneke and M. Vollrath, “What does the driver look at? The influence of intersection characteristics on attention allocation and driving behavior,” Accident Analysis and Prevention, vol. 45, pp. 610–619, 2012.
- D. M. Neyens and L. N. Boyle, “The effect of distractions on the crash types of teenage drivers,” Accident Analysis and Prevention, vol. 39, no. 1, pp. 206–212, 2007.
- K. L. Young and P. M. Salmon, “Examining the relationship between driver distraction and driving errors: a discussion of theory, studies and methods,” Safety Science, vol. 50, no. 2, pp. 165–174, 2012.
- S. Sarkar and M. Andreas, “Acceptance of and engagement in risky driving behaviors by teenagers,” Adolescence, vol. 39, no. 156, pp. 687–700, 2004.
- R. P. Compton and P. Ellison-Potter, Teen Driver Crashes: A Report to Congress, Office of Behavioral Safety Research, NHTSA, Washington, DC, USA, 2008.
- J. K. Caird, S. L. Chisholm, C. J. Edwards, and J. I. Creaser, “The effect of yellow light onset time on older and younger drivers' perception response time (PRT) and intersection behavior,” Transportation Research F, vol. 10, no. 5, pp. 383–396, 2007.
- C. Y. D. Yang and W. G. Najm, “Examining driver behavior using data gathered from red light photo enforcement cameras,” Journal of Safety Research, vol. 38, no. 3, pp. 311–321, 2007.
- G. S. Rubin, E. S. W. Ng, K. Bandeen-Roche, P. M. Keyl, E. E. Freeman, and S. K. West, “A prospective, population-based study of the role of visual impairment in motor vehicle crashes among older drivers: the SEE study,” Investigative Ophthalmology and Visual Science, vol. 48, no. 4, pp. 1483–1491, 2007.
- B. González-Iglesias, J. A. Gómez-Fraguela, and M. Á. Luengo-Martín, “Driving anger and traffic violations: gender differences,” Transportation Research F, vol. 15, no. 4, pp. 404–412, 2012.
- N. Elmitiny, X. Yan, E. Radwan, C. Russo, and D. Nashar, “Classification analysis of driver's stop/go decision and red-light running violation,” Accident Analysis and Prevention, vol. 42, no. 1, pp. 101–111, 2010.
- B. E. Porter and T. D. Berry, “A nationwide survey of self-reported red light running: measuring prevalence, predictors, and perceived consequences,” Accident Analysis and Prevention, vol. 33, no. 6, pp. 735–741, 2001.
- B. G. Simons-Morton, M. C. Ouimet, Z. Zhang et al., “The effect of passengers and risk-taking friends on risky driving and crashes/near crashes among novice teenagers,” Journal of Adolescent Health, vol. 49, no. 6, pp. 587–593, 2011.
- R. L. Olson, R. J. Hanowski, J. S. Hickman, and J. Bocanegra, “Driver distraction in commercial vehicle operations,” Tech. Rep. Fmcsa-rrr-09-042, U.S. Department of Transportation, Washington, DC, USA, 2009.
- C. Hallett, A. Lambert, and M. A. Regan, “Text messaging amongst New Zealand drivers: prevalence and risk perception,” Transportation Research F, vol. 15, no. 3, pp. 261–271, 2012.