research paper on distracted driving

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Published: 01 March 2016

Texting while driving: the development and validation of the distracted driving survey and risk score among young adults

Regan W. Bergmark ORCID: orcid.org/0000-0003-3249-4343 1 , 2 , 3 ,
Emily Gliklich 1 ,
Rong Guo 2 , 3 &
Richard E. Gliklich 1 , 2 , 3

Injury Epidemiology volume 3 , Article number: 7 ( 2016 ) Cite this article

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Texting while driving and other cell-phone reading and writing activities are high-risk activities associated with motor vehicle collisions and mortality. This paper describes the development and preliminary evaluation of the Distracted Driving Survey (DDS) and score.

Survey questions were developed by a research team using semi-structured interviews, pilot-tested, and evaluated in young drivers for validity and reliability. Questions focused on texting while driving and use of email, social media, and maps on cellular phones with specific questions about the driving speeds at which these activities are performed.

In 228 drivers 18–24 years old, the DDS showed excellent internal consistency (Cronbach’s alpha = 0.93) and correlations with reported 12-month crash rates. The score is reported on a 0–44 scale with 44 being highest risk behaviors. For every 1 unit increase of the DDS score, the odds of reporting a car crash increases 7 %. The survey can be completed in two minutes, or less than five minutes if demographic and background information is included. Text messaging was common; 59.2 and 71.5 % of respondents said they wrote and read text messages, respectively, while driving in the last 30 days.

The DDS is an 11-item scale that measures cell phone-related distracted driving risk and includes reading/viewing and writing subscores. The scale demonstrated strong validity and reliability in drivers age 24 and younger. The DDS may be useful for measuring rates of cell-phone related distracted driving and for evaluating public health interventions focused on reducing such behaviors.

Texting and other cell phone use while driving has emerged as a major contribution to teenage and young adult injury and death in motor vehicle collisions over the past several years (Bingham 2014 ; Wilson and Stimpson 2010 ). Young adults have been found to have higher rates of texting and driving than older drivers (Braitman and McCartt 2010 ; Hoff et al. 2013 ). Motor vehicle collisions are the top cause of death for teens, responsible for 35 % of all deaths of teens 12–19 years old, with high rates of distraction contributing significantly to this percentage (Minino 2010 ). In 2012, more than 3300 people were killed and 421,000 injured in distraction-related crashes in the US, with the worst levels of distraction in the youngest drivers (US Department of Transportation National Highway Traffic Safety Administration 2014 ).

While distracted driving includes any activity that takes eyes or attention away from driving, there has been particular and intense interest on texting and other smartphone-associated distraction as smartphones have become widely available over the past ten years. Multiple studies have examined driving performance while texting or completing other secondary tasks (Yannis et al. 2014 ; Owens et al. 2011 ; Olson et al. 2009 ; Narad et al. 2013 ; McKeever et al. 2013 ; Drews et al. 2009 ; Hickman and Hanowski 2012 ; Leung et al. 2012 ; Long et al. 2012 ). Uniformly, distraction from cell phone use, including texting, dialing or other behaviors, is associated with poorer driving performance (Yannis et al. 2014 ; McKeever et al. 2013 ; Bendak 2014 ; Hosking et al. 2009 ; Irwin et al. 2014 ; Mouloua et al. 2012 ; Rudin-Brown et al. 2013 ; Stavrinos et al. 2013 ). A 2014 meta-analysis of experimental studies found profound effects of texting while driving with poor responsiveness and vehicle control, and higher numbers of crashes (Caird et al. 2014 ). A rigorous case–control study found that among novice drivers, sending and receiving texts was associated with significantly increased risk of a crash or near-crash (O.R. 3.9) (Klauer et al. 2014 ). In commercial vehicles, texting on a cell phone was associated with a much higher risk of a crash or other safety-critical event, such as near-collision or unintentional lane deviation (OR 23.2) (Olson et al. 2009 ). Motor vehicle crash-related death and injury have also been strongly associated with texting (Pakula et al. 2013 ; Issar et al. 2013 ).

Although the dangers of texting and driving are well-established, a focused brief survey on driver-reported texting behavior does not yet exist. Multiple national surveys which include texting while driving as part of a more extensive survey on distracted driving or youth health have found that young drivers have high rates of texting while driving, often in spite of high levels of perceived risk (Hoff et al. 2013 ; Buchanan et al. 2013 ; Cazzulino et al. 2014 ; O’Brien et al. 2010 ; Atchley et al. 2011 ; Harrison 2011 ; Nelson et al. 2009 ). The surveys confirm that young adults are at high risk for distracted driving; in one, 81 % of 348 college students stated that they would respond to an incoming text while driving, and 92 % read texts while driving (Atchley et al. 2011 ). Among several large survey based studies, the National Highway Traffic Safety Administration reported from a 2012 survey that nearly half (49 %) of 21–24 year old drivers had ever sent a text message or email while driving (Tison et al. 2011 -12), and even more alarming, the Centers for Disease Control and Prevention (CDC)’s National Youth Risk Behavior Survey found that nearly as many high school students who drove reported texting in just the past 30 days (41.4 %) ( Kann et al. 2014 ). The problem is not confined to novice drivers. Among US adults ages 18 to 64 years 31 % report reading or sending text messages or emails while driving in prior last 30 days ( Centers for Disease Control and Prevention (CDC) 2013 ).

Given the magnitude of the problem, a very brief questionnaire focused on texting and driving for evaluation of public health measures such as anti-texting while driving laws, cell phone applications and public health campaigns would be useful. The use of self-reported validated surveys is an increasingly common approach to understanding health issues as well as their response to intervention (Guyatt et al. 1993 ; Tarlov et al. 1989 ; Stewart and Ware 1992 ). Current surveys are driving-specific but lengthy and potentially prohibitive for widespread dissemination (Tison et al. 2011 -12, McNally and Bradley 2014 ; Scott-Parker et al. 2012 ; Scott-Parker and Proffitt 2015 ), do not include texting as a survey domain within the realm of distraction (Martinussen, et al, 2013 ), are general health surveys without sufficient information on texting and driving ( Kann et al. 2014 ), or have not been designed or validated to reliably measure and evaluate individual crash risk ( Kann et al. 2014 ). For example, a new survey of reckless driving behavior includes information on multiple driving-related domains of behavior, but administration takes 35 min and the survey does not focus on cell phones (McNally and Bradley 2014 ). Another survey of distraction in youth is similarly comprehensive without a focus on phone use (Scott-Parker et al. 2012 ; Scott-Parker and Proffitt 2015 ). The goal of shorter surveys for evaluation of distracted driving has been well documented and development of the mini Driver Behavior Questionnaire (Mini-DBQ) is an example, although it does not address cell phone related distracted driving (Martinussen et al. 2013 ). However, many interventions target cell phone use specifically rather than distraction broadly. In addition, most surveys do not delve into the specific timing of texting while driving that allows a more precise estimate of the behavior’s prevalence.

The purpose of this study was to develop a reliable self-reported survey for assessing levels of cell phone related distracted driving associated with viewing and typing activities and to validate it in a higher risk population of drivers age 24 years or younger.

Study design and oversight

A literature review and open-ended interviews with experienced and novice drivers were performed to identify the most common domains for item development as well as any existing survey items with validation metrics. The literature review was performed with reviewing terms including “Text*” and “Driv*” reviewing for any studies that included driver-reported outcomes. Initial items were piloted with open-ended responses. Ten novice (18–25 years old) and experienced (30 years old or older with at least 10 years of driving experience) drivers underwent semi-structured interviews about cell phone use while driving to further generate potential survey domains. Text messaging through various applications, map/GPS use, email and social media were prominent themes. “Texting while driving” was interpreted very differently by various participants; some people stated that texting at stop lights or at slow speeds, or reading texts, did not really constitute texting and driving. This finding suggested that a questions that simply asks “do you text and drive?” may be missing a significant proportion of this distracted behavior.

Based on the identified themes, we developed a series of Likert scale and multiple-option items reflecting the most common reading and typing tasks reported on a cell phone (Table 1 ). The format of many of our questions was modeled on the Centers for Disease Control and Prevention National Youth Risk Behavior Survey and after a thorough review of the other surveys described above. The assessed activities included reading or viewing text messages, emails, map directions, internet sites and social messaging boards and typing or writing activities through these same applications. The piloting process revealed that in addition to questions addressing frequency of the activity over the previous 30 days while driving (e.g. every time, most of the time, etc.), it was important to also assess when the activities were performed with respect to vehicular motion or speed (any speed, low speeds, stop and go traffic, etc.) to allow for further risk stratification. Additional items assessed driver attitudes with respect to their perceived level of risk associated with performing these activities. The questionnaire was pre-tested with 30 drivers 18–24 years old and went through multiple iterations. In addition to questions on cell phone reading and writing activities, the questionnaire included demographic information, self-reported “accidents” within the past 12 months of any cause, and potentially high-risk activities such as driving under the influence of alcohol or other substances. Given the colloquial use of the phrase “car accident,” we used the term “car accident” in our survey, but in the results section refer to this number as the crash rate. The question included in the final survey to elicit crash data was, “In the last 12 months, have many car accidents have you been in with you as the driver? (Answers 1, 2, 3, 4, 5 or more).” Based on feedback from the pilot testing, twenty-nine items were selected for testing in the initial questionnaire.

The questionnaire was set up as a web-based survey using standard, HIPAA compliant software. Participants provided informed consent and received a nominal incentive for participating. The study was approved by the Massachusetts Eye and Ear Institutional Review Board.

Participants

Three pools of participants 18–24 years old who had driven in the prior 30 days were recruited: (1) greater Boston metropolitan area were recruited from educational or recreational centers in the greater Boston area with flyers, enrolled through a generic link, and completed a second survey at 14 days for test-retest reliability, after which several questions were eliminated yielding and 11-item questionnaire (2) A panel was used through the software program to recruit participants from two geographic locations, (a) Eastern and (b) Western United States for a larger geographical distribution for further validation. These participants completed the survey a single time.

Item selection: reliability and validity

With the goal of creating a brief and targeted survey, items were selected for inclusion in the total score based on multiple reliability and reliability measures (Table 1 ). Item response distribution was examined prior to analysis. Items with low test-retest reliability in the Boston sample defined as a Spearman correlation of less than 0.4 or a Kappa coefficient below 0.3 were eliminated. Internal consistency was measured with Cronbach’s alpha, examining Cronbach’s alpha for each item and the DDS coefficient with each variable deleted, with any questions with a Cronbach’s alpha under 0.8 eliminated. In addition to face validity, the survey was assessed for criterion-related validity by use of concurrent validity against hypothesized correlates to other assessed variables. We hypothesized a significant correlation to self-reported crashes in the prior 12 months. We additionally postulated that writing related activities would be higher risk than reading or viewing activities alone. Conversely, we hypothesized non-significant correlations with other items (e.g. falling asleep while driving).

Items not focused on cell phone writing and reading behaviors or crash rate also were eliminated from the final survey to allow for brevity. The final survey was then tested in two cohorts of young drivers to confirm internal consistency, time required for survey completion and correlation with crash rate.

Statistical analysis

All data analysis was performed using SAS V9.4 (SAS Institute Inc., Cary, NC). Standard descriptive statistics were reported, mean (SD) for numerical variables, median (min – max) for Likert scale variables and frequency count (%) for categorical variables. The statistical underpinnings of patient-reported outcomes measures and survey design are well established; the reader may reference Fleiss’s Design and Analysis of Clinical Experiments for a detailed discussion of the methods chosen for this study (Fleiss 1999 ).”

An algorithm was created to generate a total Distracted Driving Survey (DDS) score based on the final items selected for the questionnaire where zero represents the lowest possible score. The response for each of the questions included was given a value 1–5 with 1 being the lowest risk answer (ie, no texting and driving) and 5 being the highest risk. For a given subject, the scores for the questions were then summed and reduced by the number of questions such that the lowest score was zero. The final survey, consisting of 11 questions, therefore had a range of possible scores ranging from 0 to 44, with 44 being the highest risk. In addition, two subscores for reading only (DDS-Reading) and writing only (DDS-Writing) related questions were created for further risk stratification based on evidence that writing texts is even more dangerous than reading texts alone (Caird et al. 2014 ). Wilcoxon tests were used for the comparison of DDS score by levels of demographic and behavior variables. In addition, logistic regression was performed to evaluate the effect of DDS score on reported car crashes while adjusting for driving under substance influence.

Study population

There were 228 subjects included in the study (Table 2 ). Of the Boston group, 70 of 79 initial respondents completed the survey at the two-week interval and 14 respondents were additionally excluded for reporting not having driven a motor vehicle in the prior 30 days on one or both surveys. Therefore there were a total of 56 Boston respondents (25 male, 31 female). There were 90 respondents in the Eastern Region and 82 in the Western region.

Of the 228 total respondents, 120 (52.3 %) were female. Participants self-identified as White (63.3 %), Asian (11.4 %), Black/African American (8.0 %) or other (17.3 %). 34 (15.0 %) described themselves as Hispanic. Respondents said their driving was predominantly urban (45.6 %), suburban (44.3 %), or rural (10.1 %). Most (71.5 %) respondents were either in college or had completed some or all of college. Other participants were in or had completed high school (26.3 %), or described their educational status as other (2.2 %).

Item selection: reliability

The survey was first tested in a Boston metropolitan area cohort ( N = 56) and items were reduced based on Cronbach’s alpha and the Kappa statistic (Tables 3 and 4 ). Eliminated questions asked about use of voice recognition software and riding with a driver who texted, as well as use of specific anti-texting programs, all of which did not meet reliability or validity criteria. To keep the survey brief and focused, questions that were not cell-phone specific were also eliminated (i.e., drowsiness when driving, driving under the influence, seatbelt use) even though these questions were statistically reliable. There were 11 items in the final questionnaire; the Spearman correlation coefficient for test-retest reliability was excellent at 0.82 for the final survey based on the Boston data ( N = 56) (Tables 3 , 4 and 5 ).

The DDS-Reading or viewing subscore included six items (2–6, 11). The DDS-Writing subscore included four items that asked about specific writing activities including writing texts and emails and at what speeds (7–10). The Spearman coefficient for the DDS-Reading subscore was similar at 0.82 but lower for the DDS-Writing subscore at 0.63 (Table 5 ). Strong agreement was generally observed for the items included in the DDS. In addition, very good agreement was observed for most of the variables used for concurrent validity testing of the DDS including reported crashes in the last 12 months (Kappa = 0.6).

Internal consistency

The 11-item survey with additional demographic questions was then tested in the Eastern and Western US populations. Standardized Cronbach’s alpha for the final 11-item DDS was excellent at 0.92 ( N = 228) (Table 5 ). The DDS-Reading subscore standardized Cronbach’s alpha was 0.86. The DDS-Writing score standardized Cronbach’s alpha coefficient was 0.85.

Score distribution and association with car crashes

The 11-item questionnaire was then used to calculate the DDS score as described in the methods section with a higher score indicating more risk behaviors. Mean DDS score based on the entire cohort ( N = 228) was 11.0 points with a standard deviation (SD) of 8.99 and a range of 0 to 44 points. The distribution of scores is shown in Fig. 1 . There was no statistically significant difference of DDS total score by region ( p = 0.81). The mean scores for were similar for Boston (11.2, standard deviation 7.14), Eastern United States (11.4, standard deviation 9.48), and Western United States (10.5, standard deviation 9.62).

Distribution of the Distracted Driving Survey (DDS) scores. Scores reflect the final 11-item questionnaire, calculated with a range of 0 to 44 with high scores indicating more distraction

Reading and writing scores specific subscores were also calculated and also significantly correlated with crash rate (Table 5 ). Mean writing score was 3.2 (SD 3.48, range 0–16), and mean viewing reading score was 6.57 (SD 5.16, range 0–24).

A higher DDS score indicating higher risk behavior was significantly associated with the self-reported car crashes (Wilcoxon rank sum test, p = 0.0005). Logistic regression was performed with reported car crashes as the dependent variable and DDS as the independent variable. For every one point increase of the DDS score, the odds of a self-reported car crash increased 7 % (OR 1.07, 95 % confidence interval 1.03 – 1.12, p = 0.0005). The odds ratio for the DDS-Writing subscore (OR 1.17) was the highest among the scores and subscores. As anticipated, DDS score was not significantly associated with either falling asleep while driving ( p = 0.11) or driving under the influence ( p = .09) in the Boston group ( N = 56), and these questions were eliminated for the Eastern and Western US groups.

In order to better characterize the risk of higher DDS, the DDS-11 score was categorized into < =9, 9–15 and >15 using its median (9 points) and third quartile (15). The odds of car crash for subjects with DDS-11 > 15 is 4.7 times greater than that of subjects with DDS score < =9 (95 % CI 1.8–12.6).

Texting and driving behavior

In this cohort of 228 18–24 year old divers (Table 5 ), we found that 59.2 % reported writing text messages while driving in the prior 30 days. Of the 228 drivers, most wrote text messages never or rarely, while 16 % said they write text messages some of the times they drive and 7.4 % said they write text messages most or every time they drive. When all participants were asked about the speeds at which they write text messages, 9.7 % said they write text messages while driving at any speed and an additional 24.1 % said they write text messages at low speeds or in stop and go traffic, with the remainder writing text messages only at stop lights or not writing text messages while driving at all.

Reading text messages was even more common, with 71.5 % of participants saying they read text messages while driving in the past 30 days – 29.0 % rarely, 27.2 % sometimes, 13.2 % most of the time, and 2.2 % every time they drove. Compared to writing texts, a higher percentage read text messages at any speed (12.7 %) and at low speeds (15.6 %), in stop and go traffic (10.1 %), as well as when stopped at a red light (36.3 %). Reading and writing email and browsing social media were less common. Maps were used on a phone by 74.6 % of respondents in the last 30 days.

In contrast to yes/no answers in other surveys about safety of texting and driving, this study found that only 36.4 % of respondents said it was never safe to text and drive. Drivers reported that it was safe to text and drive never (36.4 %) rarely (27.6 %), sometimes (20.2 %), most of the time (8.8 %) and always (7.0 %).” This is in contrast to yes/no answers in other surveys about texting and driving safety.

The purpose of this study was to create a short validated questionnaire to assess texting while driving and other cell-phone related distracted driving behaviors. The Distracted Driving Survey developed in this study proved to be valid and reliable in a population of 18–24 year old drivers, with excellent internal consistency (Cronbach’s alpha of 0.93). The DDS has excellent internal consistency defined as Cronbach’s alpha =0.9 or greater and strong test retest reliability.(Kline 1999 ) The Mini-DBQ, a valid measure which does not include texting or other cell-phone related distracted driving, is considered a valid measure with Cronbachs alpha of less than 0.6, substantially lower than the DDS (Martinussen et al. 2013 ).

The Distracted Driving Survey score was significantly correlated with self-reported crash rates in the prior 12 months with people in the highest tercile of derived scores (here, those with a score >15) more than 4.7 times as likely to have had a crash than subjects with scores in the lowest tercile of risk (here, those <9). Stepwise logistic regression demonstrated this relationship to have a ‘dose response’, with higher scores incrementally associated with higher crash rates. The odds of a reported crash increased 7 % for every increase of one point of the DDS score (OR 1.07, 95 % confidence interval 1.03 – 1.12, p = 0.0005). This relationship was further demonstrated to be independent of such factors as driving under the influence of alcohol or other substances, and falling asleep while driving.

The DDS confirmed prior reports of high levels of texting while driving, and further elucidated specific aspects of the behavior including to what extent people read versus write text messages and and what speeds they perform these activities. 59.2 and 71.5 % of respondents said they wrote and read text messages, respectively, while driving in the last 30 days. Respondents were most likely to do these activities while stopped, in stop-and-go traffic or at low speeds although a small percentage said they have read or written text messages while traveling at any speed. Prior studies have shown high rates of texting while driving in spite of high rates of perceived risk. In this study, Likert-scale questions further demonstrated that most respondents actually felt that texting and driving can be safe at least on rare occasions; only 36.4 % of respondents said it was always unsafe to text and drive. These data correspond more directly to the amount of texting and driving reported here including reading or writing texts while stopped or in stop and go traffic.

Texting and other cell phone use while driving is frequently targeted as a public health crisis, but many of these interventions have unclear impact. Since the advent of the Blackberry in 2003 and the first iPhone in 2007, texting and driving has been highlighted in the news and by cell phone carriers, such as with AT&T’s It Can Wait pledge, to which more than 5 million people have committed (AT&T 2014 ). There are multiple smartphone applications and other interventions aimed at reducing texting and driving (Verizon Wireless 2014 ; Lee 2007 ; Moreno 2013 ), and Ford has even created a Do Not Disturb button in select vehicles blocking all incoming calls and texts (Ford 2011 ). Forty-four U.S. states and the District of Columbia ban texting and driving, with Washington State passing the first ban in 2007 (Governors Safety Highway Association 2014 ), and there is a push for even more aggressive laws and enforcement (Catherine Chase 2014 ). Texting bans have been shown to be effective in some studies. Texting bans are associated with reductions in crash-related hospitalizations (Ferdinand et al. 2015 ). Analysis of texting behavior from the U.S. Centers for Disease Control and Prevention 2013 National Youth Risk Behavior Survey showed that text-messaging bans with primary enforcement are associated with reduced texting levels in high school drivers, whereas phone use bans were not (Qiao and Bell 2016 ). Other studies surveying drivers have found a mixed response of whether behavior is altered, with some drivers not altering their behavior (Mathew et al. 2014 ). However, the impact of many of these interventions has not yet been studied or fully understood. While driver reported surveys exist today, in general these instruments have high respondent burden and have not been designed or validated for individual measurement.

We aimed to develop a validated, reliable and brief survey for drivers to report and self-assess their level of risk and distraction to fill gaps in the literature and facilitate standardized measurement of behavior. Initial validation detailed here focused on a population of young drivers most at risk for motor vehicle crashed and deaths. Survey development was carefully undertaken here with semi-structured interviews, pilot testing and testing of young adults in a major metropolitan area as well as in the Western and Eastern United States. Validity and reliability were measured in multiple ways. While there are multiple functions associated with cell phone use that can be distracting to a driver, we focused on typing and reading or viewing activities as those have been both extensively studied and demonstrated to have significant effect sizes in the simulator literature (Caird et al. 2014 ).

The resulting survey is brief and easy to administer. In automated testing, the full research survey required approximately four and a half minutes to complete and completing the 11-item DDS component takes around two minutes. In actual testing, all respondents were able to complete the survey.

This survey provides self-reported data from young US drivers in a relatively small sample size of 228 drivers age 18–24. Participants voluntarily took the survey so it is possible that the type of driver who took the survey may be more attuned to the risks of texting and driving or that there may be some other selection bias. Tradeoffs were made in the comprehensiveness of the questions selected to purposefully construct a brief instrument, with intentional elimination of questions on certain functions of cell phone use and other forms of distraction. For example, this study did not quantify the driving patterns of the respondents in the prior 30 days. Respondents who had not driven in the last 30 days were excluded. Because this study aimed to validate this survey among young people age 18–24, there are college students included who may have more limited driving patterns. Further studies are needed to validate this survey among drivers of all ages. This survey did not aim to quantify the number of texts or viewing time per mile. Further studies could be done to validate this survey against quantitative measures of viewing and reading behavior, which was beyond the scope of this study. However, the high Cronbach’s alpha and other characteristics suggest that the resulting brief instrument is well suited for large population studies that seek to limit respondent burden. Further research will likely lead to refinement in the scoring algorithms used. The performance of the DDS has not yet been studied in older age groups. Strengths of the study include good ethnic representation closely aligned with US census data and an anonymous format conducive to more accurate reporting of these behaviors.

The DDS is intended to be used to assess behavior patterns and risk and to evaluate the impact of public health interventions aimed at reducing texting and other cell phone-related distracted driving behaviors. The DDS score demonstrated strong performance characteristics in this validation study. Further research is needed to evaluate the instrument in larger and more diverse populations and to evaluate its sensitivity to change following interventions. Since a DDS score can be immediately generated at the time the DDS is completed, another area of research is whether the score itself may have value as an intervention.

The Distracted Driving Survey is a brief, reliable and validated measure to assess cell-phone related distraction while driving with a focus on texting and other viewing and writing activities. This survey is designed to provide additional information on frequency of common reading and viewing activities such as texting, email use, maps use, and social media viewing. The data are informative because different anti-distraction interventions target various aspects of cell phone utilization. For example, some anti-texting cell phone applications would not affect maps viewing, email viewing or writing, or social media use and therefore would not impact those behaviors. Further research is required to determine if these trends also hold true for older drivers. Higher DDS scores, indicating more distraction while driving, were associated with an increase in reported crashes in the prior 12 months in a dose–response relationship. Although this finding does not prove causality, the association is concerning and corroborates other studies demonstrating the risks of texting on crash rates on courses and simulators. This study confirmed prior reports of high rates of texting and driving in a young population, with more detailed reports of behavior on writing and reading text messages, the speeds at which these activities are performed, and respondents’ perception of risk. This measure may be used for larger studies to assess distracted driving behavior and to evaluate interventions aimed at reducing cell phone use, including texting, while driving. An improved understanding of the common cell phone functions used by young drivers should be used to inform the interventions aimed at reducing cell phone use while driving.

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Bergmark, R.W., Gliklich, E., Guo, R. et al. Texting while driving: the development and validation of the distracted driving survey and risk score among young adults. Inj. Epidemiol. 3 , 7 (2016). https://doi.org/10.1186/s40621-016-0073-8

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An evidence-based review: distracted driver

Affiliation.

1 From the University of South Florida (L.E.L.), Tampa, Florida; Westchester Medical Center (K.A.), Valhalla, New York; East Carolina University (M.B.), Greenville, North Carolina; Advocate Medical Group (S.S.), Chicago, Illinois; Howard University Hospital (W.G.), Washington, District of Columbia; and Johns Hopkins University (A.D., A.S.), Baltimore Maryland; and Aga Khan University Hospital (J.B.), Karachi, Pakistan Karen Hospital Consulting Clinics (J.M.), Nairobi, Kenya.
PMID: 25539216
DOI: 10.1097/TA.0000000000000487

Background: Cell phone use and texting are prevalent within society and have thus pervaded the driving population. This technology is a growing concern within the confines of distracted driving, as all diversions from attention to the road have been shown to increase the risk of crashes. Adolescent, inexperienced drivers, who have the greatest prevalence of texting while driving, are at a particularly higher risk of crashes because of distraction.

Methods: Members of the Injury Control Violence Prevention Committee of the Eastern Association for the Surgery of Trauma performed a PubMed search of articles related to distracted driving and cell phone use as a distractor of driving between 2000 and 2013.

Results: A total of 19 articles were found to merit inclusion as evidence in the evidence-based review. These articles provided evidence regarding the relationship between distracted driving and crashes, cell phone use contributing to automobile accidents, and/or the relationship between driver experience and automobile accidents. (Adjust methods/results sections to the number of articles that correctly corresponds to the number of references, as well as the methodology for reference inclusion.)

Conclusion: Based on the evidence reviewed, we can recommend the following. All drivers should minimize all in-vehicle distractions while on the road. All drivers should not text or use any touch messaging system (including the use of social media sites such as Facebook and Twitter) while driving. Younger, inexperienced drivers should especially not use cell phones, texting, or any touch messaging system while driving because they pose an increased risk for death and injury caused by distractions while driving.

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July 28, 2023

Distracted Driving Is More Dangerous Than People Realize, New Research Shows

Multitasking can occupy attention longer than people anticipate

By Tatiana Koerich Rondon & David Z. Hambrick

A person driving an automobile, one hand on the steering wheel, one hand holding up a smartphone

skaman306/Getty Images

In 2021 more than 3,500 drivers in the U.S. alone died in traffic accidents linked to distracted driving . Using a cell phone is the primary source of distraction , but entering navigational information, trying to eat and performing other such activities can be just as risky. A new study in the Journal of Experimental Psychology: Applied suggests that distracted driving is even more unsafe than previously thought .

Multitasking has a hidden cost for drivers that past analyses have not taken into account. In two experiments, participants between the ages of 18 and 58 completed a driving-related activity while also performing a distracting task. Cognitive psychologists led by David Strayer of the University of Utah found that distraction depleted participants’ ability to pay attention to their driving for at least half a minute after the distraction ended . That extended effect implies that the number of traffic accidents caused by distracted driving could be substantially higher than current estimates indicate.

[ Read more about how the brain works to suppress distractions ]

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In the first experiment, 32 participants began with a “baseline” phase, during which the researchers asked them to use a steering wheel to position a triangle over a dot that was moving horizontally across a computer screen. This simple activity captured a key aspect of driving, steering, while keeping participants in a safe laboratory environment. Simultaneously, people had to press a button on the steering wheel with their left forefinger each time a small device attached to their left collarbone vibrated. This extra step measured how much attention participants devoted to the primary activity, driving, as opposed to the secondary activity. Those who responded relatively slowly to the vibrations were assumed to be paying more attention to tracking the dot on the screen.

After three minutes in this baseline condition, participants transitioned to an “on-task” phase. During this period, they experienced a distraction designed to simulate the attention-demanding tasks that occur when people are keeping up a cell phone conversation or sending a text while driving. The “drivers” were presented witha random number and had to count aloud backward by either ones or threes. After 20 seconds of this challenging phase, there was a 30-second recovery period in which the backward counting task stopped, and participants only performed the driving and vibration-response activity.

Compared with baseline, the distracting on-task phase did not appear to affect the participants’ performance in tracking the moving dot—but they were slower and less accurate in their response to the vibrations. This finding indicates that more of the participants’ attention was occupied when they were trying to drive and count at the same time. More surprisingly, their performance remained impaired in the recovery period, too. In other words, even though they were no longer multitasking, people were still slower and less accurate in responding to the vibrations than in the baseline phase. This residual effect of multitasking was largest at the beginning of the recovery phase but still evident at the end of the 30 seconds .

A second experiment involving 47 participants had essentially the same design as the first, except that the primary task involved a realistic driving simulator. This approach gave the researchers a chance to observe responses in more true-to-life scenarios, including driving in light and heavy traffic. In addition, the researchers used special equipment to observe their participants’ eyes. When people are cognitively busy, their pupils expand, giving researchers an indicator of attentional engagement.

Once again, the distraction involved a backward-counting task, followed by a recovery period, now extended to 45 seconds. Just as in the first experiment, the researchers found that people performed worse throughout the recovery period than in the baseline phase. The driving task was especially difficult when the simulator put drivers in heavy traffic. For example, participants had more difficulty staying centered in their lane in the challenging driving simulator. Their pupils also dilated significantly during the distracting counting task and remained so throughout most of the recovery period. In fact, participants in the most challenging simulation showed dilation throughout the 45-second period. These findings provide additional evidence for the residual effect of multitasking.

What might explain these findings? When a person performs a cognitive task, they hold information from that task in their working memory: a “mental workspace” where details can be both stored and processed. Your working memory helps you with tasks such as doing arithmetic in your head and remembering the name of someone you’ve just met. Strayer and his colleagues propose that when a task is completed, this information isn’t purged from your working memory all at once. Rather it persists for some time, creating mental clutter that may divert attention away from subsequent tasks.

This work complements a large body of evidence that shows people tend to be bad at multitasking . (In fact, people are generally worse at juggling tasks simultaneously than they believe themselves to be.) If you’ve ever been working and gotten distracted by an incoming e-mail, you may have felt a “mental fog” when you switched back to your earlier task. That experience could have occurred because your mind was still holding details from reading your inbox even as you got back to work.

The findings also mean that drivers likely underestimate the true danger of distraction. If you send a text while driving, even though you may not miss your exit (or worse), you will be at a heightened risk of doing so down the road. Similarly, sending an e-mail while sitting at a traffic light means that once the light turns green, your mind will still be occupied by that message.

The new research highlights the need to strengthen laws that curb distracted driving. Legislation should define this concept broadly enough to include not only cell phone use but also other activities that can divert a driver’s attention away from the road. Of course, not all distractions come from technology, which is why all passengers should be aware of how hazardous distracting the person at the wheel can be. Parents, for instance, can set ground rules for kids in the car.

Finally, the findings point to a simple step that all drivers can take to make the roads safer. The next time you get behind the wheel, minimize the distraction you will experience by putting your cell phone in airplane mode, entering navigation information and finishing your lunch before you start the engine.

Are you a scientist who specializes in neuroscience, cognitive science or psychology? And have you read a recent peer-reviewed paper that you would like to write about for Mind Matters? Please send suggestions to  Scientific American’s Mind Matters editor Daisy Yuhas at  [email protected] .

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

Detection of Driving Distractions and Their Impacts

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Distracted Driving Research

Cell phones and passengers are a major source of inattention to the roadway and are a contributing factor in motor vehicle crashes involving teens behind the wheel. Engaging in handheld cell phone use increases the risk of a crash substantially, and distracted driving research is much needed to prevent crashes involving young drivers. Investigators at the Penn Injury Science Center and the Annenberg Public Policy Center collaborated with CIRP researchers to develop behavior change interventions that include strategies to encourage focused attention on driving.

These studies tested different interventions aimed at reducing distracted driving , particularly in teen drivers, including a web-based educational intervention and behavioral economics strategies, as well as different ways to implement technological solutions, such as automated blocking of cell phone use and silencing of notifications while the vehicle is in motion. Outcomes for these studies are measured either through use of a driving simulator and/or a smartphone application and paired in-vehicle device.

Learn about current research being conducted at the Penn Injury Science Center.

Research Projects

Novel smartphone applications exist that can limit handheld cell phone use while the vehicle is in motion by locking the phone screen, silencing notifications, and sending automated responses to incoming text messages. These applications work by detecting when the vehicle is traveling over a certain speed threshold. In some cases, hands-free use of navigation and music apps may be permitted if programmed at the beginning of the drive or while stopped. We are testing different ways to implement this technology to sustainably reduce cell phone use distraction in the long run. Ongoing pilot studies of distracted driving research include:

Way to Safety 3.0: The Teen-Parent Study

Teen drivers often tell researchers their parents urge them not to text while driving because it is dangerous, yet the teens frequently see them checking email or texting while driving. In this study, researchers are using a smartphone application that tracks and limits cell phone use while driving. Parents are notified via email when their teen overrides the cell phone blocking function. The study tests the feasibility of an intervention in which teens also receive an email alert when their parents override the cell phone blocking function. The overall goal of the research is to reduce rates of cell phone use while driving for the entire family.

Way to Safety 2.0: Opt-in vs. Opt-out Cellphone Blocking +/-Notifications

This study tests the feasibility of different cell phone blocking strategies among teen drivers. The first is “opt-in” blocking, which is similar to using “Airplane Mode:” Drivers must remember to turn on the app when getting in the car. The second is “opt-out” blocking: The app activates automatically when the vehicle is in motion. The third strategy is “opt-out” blocking with notifications: This is the same as the second strategy, but parents will also be alerted when the teen overrides the blocking strategy.

Distracted Driving App Study

This study examines the effectiveness of a distracted driving smartphone application for teen drivers and their parents. The app activates automatically when the vehicle is in motion, uses innovative recognition software to determine if the phone-user is the passenger or driver of the vehicle, and locks the phone while the user is driving.

The majority of U.S. teens admit to handheld cell phone use while driving, an increasingly common cause of crashes. Attitudes towards novel cell phone applications and settings that block use while driving are poorly understood, potentially limiting their use. The researchers examined teens' willingness to reduce cell phone use while driving and perceptions of potential strategies to limit this behavior via a survey.

Read the study abstract

Read the press release

This program of research uses a web-based intervention based on the Theory of Planned Behavior that aims to reduce distracted driving and teen driver inattention to the roadway. Researchers developed the intervention from elicitation and survey data from previous work and empirical evidence in the literature. The results of this formative research on cell phones and peer passengers were published in Traffic Injury Prevention , the Journal of Pediatric Nursing , and the Journal of School Nursing . They then pilot tested the intervention using driving simulation to test the effects of the intervention on cell phone use, passenger engagement, and driving performance.

Read a blog post about the research

Listen to a podcast about the research

This study examined novel measures of cell phone use while driving to better understand how and at what speeds adolescents use their cell phones while driving. The researchers enrolled a small group of adolescent drivers who were licensed for less than 90 days to install a device in their vehicle and an application on their smartphone to see if the study team could measure interaction with the cell phone. The researchers were able to collect data on how often the young drivers “unlocked” their cell phone, their trip length in miles and hours, and vehicle speed. Results showed engagement at high speeds, which is particularly risky for newly licensed drivers. The information gathered about how adolescent drivers engage with their phones at elevated speeds will help inform future research on the nature of risky driving behaviors and how to prevent them.

Read the study abstract

This study, funded by the Federal Highway Administration, will investigate behavioral economics strategies to reduce cell phone use behind the wheel. Behavioral economics combines insights and findings from psychology and economics to explain, and try to correct, counter-productive or predictably irrational decision-making. These strategies have been found to be successful in helping people change difficult health behaviors, such as smoking and not taking medications as prescribed.

The study team hopes to develop interventions that can be delivered through smartphones to discourage cell phone use while driving, a dangerous distraction. The researchers will investigate strategies that can be deployed in usage-based insurance (UBI) programs using data collected by Progressive Insurance based on an app developed by TrueMotion, a smartphone telematics platform.

Read a press release about the study grant.

Principal Investigators: Kit Delgado, MD ; Catherine C. McDonald, PhD, RN, FAAN

Funding: Institute for Translational Medicine and Therapeutics/Center for Health Incentives and Behavioral Economics Pilot Grants Program; University of Pennsylvania NIA Roybal Center for Behavioral Economics; National Institute of Child Health and Human Development; National Heart, Lung, and Blood Institute; National Institute of Nursing Research; the University Research Foundation at PENN; the Dr. Dorothy Mereness Endowed Research Fund at the University of Pennsylvania School of Nursing; the Federal Highway Administration.

This commentary about the Flaherty et al study, both published in the May 2020 issue of Pediatrics , agrees that the lower incidence of fatal crashes for 16- to 19-year-old drivers in states with primarily enforced texting bans is encouraging since previous research examining the association between cell phone bans and lower rates of fatal crashes in teen drivers showed mixed results. However, no one strategy will prevent cell phone use while driving in adolescents. It will take a multipronged approach, one that addresses the unique factors that contribute to distracted driving in teens behind the wheel.

Read the commentary

Authors: Catherine C. McDonald, PhD, RN, FAAN; Kit Delgado, MD ; Mark R. Zonfrillo, MD

Funding : National Institute of Child Health and Human Development

This study, conducted by researchers at Children's Hospital of Philadelphia and the Annenberg Public Policy Center at the University of Pennsylvania , revealed that young adults who use cell phones while driving also engage in other risky driving behaviors , including speeding, running red lights, and impatiently passing a car in front on the right. Published in the Journal of Environmental Research and Public Health , the research found that this pattern of risk-taking is associated with impulsivity and the authors stress the importance of developing new strategies to prevent risky driving in young adults, ones that promote save driving behaviors more broadly.

Principal Investigators: Elizabeth Walshe, PhD ; Dan Romer, PhD ; Flaura Winston, MD, PhD

Funding: Annenberg Public Policy Center at the University of Pennsylvania; Center for Injury Research and Prevention at Children's Hospital of Philadelphia

Brazil Becomes the Largest Nation to Decriminalize Marijuana

The country’s Supreme Court voted to remove criminal penalties for possession of up to 40 grams of marijuana.

A group of people marching in a street and a sign in Portuguese that says, “Legalize now!”

By Jack Nicas and Ana Ionova

Reporting from Rio de Janeiro

Brazil decriminalized marijuana for personal use on Wednesday, making the nation of 203 million the largest to take such a measure and the latest sign of a growing global acceptance of the drug.

Brazil’s Supreme Court ruled that Brazilians could possess up to 40 grams of cannabis — roughly enough for 80 joints — without facing penalties, a decision that would take effect within days and stand for the next 18 months.

The court asked Brazil’s Congress and health authorities to then set the permanent amount of marijuana that citizens could possess. Selling marijuana remains a criminal offense.

Thousands of Brazilians are serving prison sentences for possessing an amount of marijuana below the new threshold, legal analysts said. It is unclear how the decision would affect those convictions.

Many are Black men, who represent 61 percent of drug-trafficking prosecutions but 27 percent of the population. Studies have shown thousands of Black Brazilians have been convicted in situations that have led to lesser or no charges against white people.

Brazil has long taken a harsh criminal approach to drugs, so its decision to effectively allow citizens to smoke marijuana is part of a remarkable shift in public opinion and public policy on the drug over the past two decades. More than 20 countries have now decriminalized or legalized recreational use of marijuana, most in Europe and the Americas.

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Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity

Susana garcía-herrero.

1 Escuela Politécnica Superior, Universidad de Burgos, 09006 Burgos, Spain; [email protected] (W.B.); se.ubu@lacsiram (M.Á.M.S.)

Juan Diego Febres

2 Department of Chemistry and Exact Sciences, Universidad Técnica Particular de Loja, 110107 Loja, Ecuador; ce.ude.lptu@serbefdj

Wafa Boulagouas

José manuel gutiérrez.

3 Consejo Superior de Investigaciones Científicas, 39005 Santander, Spain; [email protected]

Miguel Ángel Mariscal Saldaña

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.

1. Introduction

The road transportation system presents a high-risk system that threatens people’s lives every day [ 1 ]. The severity of traffic accidents raises many social direct and indirect problems, physical and mental health disorders, economic expenses, and massive damage to the surroundings and properties [ 2 ]. The World Health Organization estimates that approximately 1.35 million people die in road traffic accidents each year (on average, 3700 people lose their lives per day), and 20 to 50 million more people suffer non-fatal injuries, which often lead to long-term disabilities [ 3 ]. In this regard, studies on the features of road safety concluded that traffic accidents occur mainly due to human factors, road infrastructure, environmental aspects, and their interactions [ 4 , 5 ]. Nevertheless, a growing body of research suggests that human factors, e.g., speeding, drink-driving, distracted driving, have the strongest influence and are responsible for 80–90% of road traffic accidents [ 6 , 7 , 8 , 9 ]. Indeed, past research reported that distracted driving contributes to over half of inattention traffic accidents [ 10 ]. This is supported by an explosion of studies. For instance, in the USA, the National Highway Safety Administration estimated that distracted driving is responsible for approximately 10% of all fatal traffic accidents [ 11 ]. Similarly, in Spain, the General Traffic Department, reported that distracted driving contributed to approximately 28% of police-reported fatal traffic accidents [ 12 ].

Placing greater importance on the fact that secondary tasks that compete for the driver’s attention and potentially degrade their perception and their ability to interpret the information they receive continually from a changing roadway [ 13 , 14 ], in the recent past, many researchers and traffic transportation experts have deeply studied aspects of distracted driving and concluded that a majority of distracted driving is related to new technologies. Furthermore, a review of the current literature on distracted driving found a specific focus on mobile phone-related distractions. For instance, a survey study reported that 42% of drivers confirmed that they answer their mobile phones when driving, and 56% admitted to continuing to drive while completing the conversation [ 15 ]. An observational study of 6578 drivers on randomly selected urban roads in Spain found that 20% of the observed drivers engaged in secondary tasks including talking on handheld mobile phones (i.e., 1.3%) [ 16 ].

A simulator study investigating the relationship between performing a secondary task (e.g., mobile phone use) and driving performance reported that engaging in a secondary task influences longitudinal and lateral control of the vehicle and leads to higher speeds [ 17 ]. Moreover, an examination of the chance of drivers colliding increases twelvefold when they handled their mobile phones [ 18 ]. Furthermore, it has been found that the effects of the use of mobile phones on drivers’ reactions are worse than driving under a 0.08% blood alcohol level [ 19 ].

Another experimental study analyzed the effects of the use of mobile phones on the reaction time of drivers in dangerous situations (pedestrian crossing and road crossing by parked vehicles, in particular) found that the use of mobile phones leads up to 204% increments in reaction times, proving that distractions decrease the driving performance [ 20 ].

However, an estimation of the contribution of distracted driving in traffic accidents causality and its impact on other unsafe driving behaviors is complex. Indeed, there are the following three major challenges [ 21 , 22 ]: (i) obtaining reliable data about pre-accident conditions is difficult, (ii) there is a lack of systemic reporting, and (iii) there are inconsistencies in the definitions, classifications, and approaches used. Dealing with these challenges, many scholars have reviewed traffic accident assessment tools and advanced techniques and algorithms to increase the efficiency and effectiveness of road safety protective measures [ 23 , 24 , 25 ]. Recently, many researchers have moved toward correlating the analysis methods with prediction techniques to model interactions between risk factors and predict potential impacts on causalities, frequencies, and the severity of traffic accidents. Such a combination considers several parameters to analyze the current conditions that are, therefore, assessed using the mean of the prediction models that contribute to mitigate the magnitude of traffic accidents and enhance the transportation system and safety strategies [ 26 ]. Among the emergent techniques, there are the Grey System Theory and Markov Model [ 2 ], Data Mining Techniques [ 27 ], Structural Time Series [ 28 ], Logistic Regression Analysis [ 29 ], and Bayesian Networks [ 30 ].

Building on these attempts, this paper focuses particularly on analyzing the influence of technology-based distracted driving on drivers’ infractions and assesses their subsequent impact on the severity of traffic accidents employing recent data on road traffic accidents in Spain.

The present study is designed to provide relevant pieces of evidence on the relationship between technology-based distracted driving and other infractions. The assessment is therefore extended to evaluate the relationships between the infractions of the distracted drivers and the severity of traffic accidents and investigate the impact of a set of factors grouped into demographics, type of vehicle, and zone.

An assessment of the influence of technology-based distractions on drivers’ infractions allows the accident risk to be estimated. In other words, it allows for, first, an appreciation of the proportion by which the probability of committing aberrant infractions and speeding would be expected to increase, provided that the driver is distracted. Second, it captures the infractions that lead to serious traffic accidents resulting in fatalities, while incorporating relevant parameters.

The remainder of this paper is arranged as follows: Section 2 reviews distracted driving; Section 3 sums up the data and methodology of the study; Section 4 provides the results of the study; Section 5 discusses the results and puts forward main findings, limitations, and future research guideline, while Section 6 concludes the paper.

2. Background

Distracted driving has emerged as a major phenomenon that compromises traffic safety. It adversely impacts driving performance, increases reaction time, and reduces control over the vehicle; thus, it accounts for 25% of severe motor vehicle accidents, leading to significant morbidity and mortality [ 31 ]. In terms of definition, distracted driving refers to the inattention of drivers and their focus on other competing activities while operating a motor vehicle, for instance, talking, smoking, texting, putting on make-up, reading, eating, using mobile phones, etc. [ 32 ].

In light of the past research, distracted driving could be either intentional and voluntary, which occurs when drivers divert their attention from the driving tasks, or involuntary due to a failure to ignore non-related stimuli that motivates drivers to become distracted [ 33 , 34 , 35 ].

Distractors have been grouped into the following four main categories [ 36 , 37 ]: (i) visual, which implies taking the eyes off the road; (ii) auditory, which prevents making the best use of hearing; (iii) manual, which considers taking the hands off the wheel; and, finally, (iv) cognitive, when losing concentration on driving.

Moreover, distractions have been classified into [ 38 , 39 , 40 ] (i) in-vehicle distractions, such as using mobile phones, interacting with an entertainment system, an iPod, radio, DVD player, operating navigation system, etc., and (ii) on-road distractions, such as roadside advertisements, crash scenes, digital billboards, etc.

Although other potential distractions are still of interest, technology-based distractions have attracted the focus of researchers who thoroughly investigated the risks associated with the use of technology devices (e.g., smartphones, wearable devices, portable devices, and in-vehicle information systems) while driving and used many approaches for risk estimation, for instance [ 41 ], surveys, simulators, phone records, on-road testing, and naturalistic studies.

A study analyzing the contribution of distracted driving to traffic accidents in the United States reported that 16% of motor vehicle crashes in which people were killed and 20% of those that caused injuries were a result of distracted driving [ 13 ]. Furthermore, a synthesis of previous studies on mobile phone use and its effects on traffic safety has identified mobile phones as leading sources of distraction that reduce driving performance as they decrease the reaction time of the drivers [ 17 , 42 , 43 , 44 ]. After investigating the driving performance of distracted drivers, many studies have concluded that distracted drivers are more likely to engage in unsafe driving behaviors and several errors and violations increase, for instance, speeding violations, right-of-way violations, and failure to stop at stop signs and red lights [ 45 , 46 , 47 ]. For a better understanding of the characteristics of distracted drivers and the frequency of being engaged in distractions, many researchers have investigated the demographics of distracted drivers. Indeed, in terms of gender, a study conducted in the UK reported that no differences have been found for many types of distractions (e.g., mobile phone conversations) [ 48 ]. Furthermore, the results of this study considered the age of the driver as a crucial predictor for most of the studied distractions (including technology-based distractions), concluding that older drivers are less likely to be distracted, unlike younger drivers. Similarly, a roadside observational survey in Melbourne [ 49 ] has found that gender does not influence distracted driving, in contrast to age, where it has been suggested that young or middle-aged drivers are the predominant group in distracted driving.

Second, despite the increasing body of research on the effects of distracted driving on traffic accidents, a large part of the literature relies mostly on survey, observational, and simulator studies, which suffer a set of limitations. First, survey studies, most of the time, target one particular group of drivers (e.g., young, male, female, etc.) or analyze distracted driving by accumulating all the groups together. Furthermore, the data collected in such survey and/or interview studies are likely to be biased [ 50 ]. Even though observational studies are typically conducted at particular places, allowing sufficient time for the observer to capture the behaviors of the drivers, they, in turn, limit the data to one location along the roadway [ 51 ]. Finally, driving simulator experiments are safe assessment procedures and provide a more realistic environment; however, traffic situations are unpredictable and uncontrollable and, therefore, simulators have limited fidelity [ 52 ].

The present study is designed to adequately remove, first, the bias of underreporting and after that deploys a machine learning technique to adequately investigate the influence of distracted driving on drivers’ infractions considering the drivers’ characteristics, the type of vehicle, and the zone, and assess the impact on the severity of traffic accidents.

3. Materials and Methods

3.1. data description.

For this study, a dataset has been prepared using official data of traffic accidents that occurred in Spain in a period of four years (2016–2019) provided by the Spanish National Transportation Department. The data have been gathered by the Civil Guard General Directorate or local police officers and include information related to the traffic accident, for instance, the date, location, type of vehicles involved in the traffic accidents, demographics of the drivers, number of fatalities, trip purpose, violations, and errors, etc.

The current sample of this analysis includes 410,974 traffic accidents involving 666,504 drivers. Details of the study sample are given in Table 1 .

Frequencies of the study sample.

Variables	Number of Cases

Car, van, all-terrain vehicles	120,831	120,261	119,755	118,491	479,338
Motorcycles, quads, quadricycles	35,222	36,051	36,319	37,467	145,059
Heavy vehicles	8906	8901	9176	8873	35,856
Other vehicles	922	759	1039	3531	6251
Total	165,881	165,972	166,289	168,362	666,504

Y < 25	21,983	21,350	20,707	21,432	85,472
25 ≤ Y ≤ 40	63,188	61,476	61,191	60,538	246,393
40 ≤ Y ≤ 60	59,857	61,660	63,696	65,369	250,582
Y > 60	17,399	18,102	18,333	19,010	72,844
Unspecified	3454	3384	2362	2013	11,213
Total	165,881	165,972	166,289	168,362	666,504

Male	119,878	120,447	120,920	122,407	483,652
Female	445,44	44,225	44,772	45,407	178,948
Unspecified	1459	1300	597	548	3904
Total	165,881	165,972	166,289	168,362	666,504

The dataset contains information about the severity of traffic accidents presented in ( Table 2 ). This information is aggregated into the following two groups: (i) Fatal accidents (FA) resulting in serious injuries (SI) or fatalities to the drivers involved in serious accidents (55545), and (ii) Minor accidents (MA) causing no injuries (NI) or resulting in slight injuries to the drivers involved in these minor accidents.

Traffic accident severity distribution.



M/NI	151,446	151,595	152,757	155,161	610,959
SI/F	14,435	14,377	13,532	13,201	55,545
Total	165,881	165,972	166,289	168,362	666,504

SI/F: Serious Injuries and/or Fatalities M/NI: Minor and/or No Injuries.

The collected information from the dataset about technology-based distractions and drivers’ infractions is given in Table 3 .

Frequencies of technology-based distractions and drivers’ infractions.




No infractions	54,405	52,131	52,054	62,566	221,156
Aberrant infractions	34,558	35,623	36,260	40,083	146,524
Unspecified	76,918	78,218	77,975	65,713	298,824
Total	165,881	165,972	166,289	168,362	666,504

No speed infractions	70,573	69,451	67,252	79,677	286,953
Speed infractions	8957	8154	8395	8117	33,623
Unspecified	86,351	88,367	90,642	80,568	345,928
Total	165,881	165,972	166,289	168,362	666,504

No distractions	41,766	41,790	41,944	42,634	168,134
Technology-based distractions	881	1029	1024	1114	4048
No technology-based distractions or unspecified	123,234	123,153	123,321	124,614	494,322
Total	165,881	165,972	166,289	168,362	666,504

3.2. Bias Identification

The dataset used to conduct this study was gathered by the Civil Guard General Directorate and local police officers who register information on traffic accidents, which is always incomplete. Indeed, many researchers have confirmed that as data on traffic accidents involving distracted drivers are collected from traffic accident reports, the real influence of this later on driving behaviors goes underestimated [ 53 , 54 ]. Furthermore, distractions are hard to prove using statistics from the police [ 55 ], because, in general, police officers only look for distractions when the consequences of traffic accidents are serious and, therefore, many distracted driving cases may not be recorded. Thus, scientific studies lead to unreliable conclusions.

To address the reporting biases, a methodology proposed in recent research [ 46 ] has been adopted. It is based on the introduction of a “dummy variable” into the model to isolate homogeneous subsamples and generate valid model and unbiased parameter estimations. Accordingly, the frequencies of “technology-based distractions” have been thoroughly analyzed. Particularly, differences between the percentage of drivers involved in severe accidents knowing the states (i.e., being or not distracted) and the percentage of drivers involved in severe accidents having unknown states (i.e., unspecified) are computed (10.86% versus 7.40%, respectively) ( Table 4 ). The differences are found to be significant; therefore, the variable “technology-based distractions” is biased, and a dummy variable is introduced to differentiate homogeneous cases. In the rest of the paper, the sensitivity analysis results will only show the probabilities of drivers’ infractions in traffic accident severity in a case of the state, “presence or absence of technology-based distractions” of dummy variable technology-based distractions. In this way, the subsamples containing unknown cases about the technology-based distractions are eliminated.

Dummy Variable Frequencies.



Presence or absence of technology-based distractions	42,647	42,819	42,968	43,748	172,182	25.83%	10.86%
Unknown	123,234	123,153	123,321	124,614	494,322	74.17%	7.40%
Total	165,881	165,972	166,289	168,362	666,504	/

3.3. Bayesian Networks

In the present study, Bayesian Networks have been deployed to model the influence of distracted driving on the severity of traffic accidents and unsafe driving behaviors. Over the last few decades, the Bayesian approach has been extensively applied to traffic safety studies, for instance, an assessment of road safety [ 56 , 57 ], an assessment of the influence of seatbelt use on the severity of traffic accidents [ 58 ], an estimation of committing an infraction due to mobile use [ 59 ], music distraction among young drivers [ 60 ], a prediction of traffic accidents [ 61 ], etc.

The Bayesian Networks model is a graphical inference methodology capable of predicting the future behavior of a particular variable based on past experience learned from the historical data source. Furthermore, they consist mainly of the following:

The qualitative aspect of the Bayesian Networks given by a directed acyclic network generally denoted as DAG (V, E), consisting of nodes (V) representing the variables related with directed edges (E), denoting the dependencies between the variables;
The quantitative aspect consists of the conditional probability of each node, where every node has parents and has a conditional probability table expressing the dependencies of the father nodes. Therefore, the joint probability distribution is expressed as follows: P ( X 1 , … , X n ) = ∏ i = 1 n P ( X 1 | X i − 1 , … , X n ) (1) P ( X ) = ∏ i = 1 n P ( X i | p a i ) (2) where i = 1 , 2 , … , n , X = { X 1 , X 2 , … , X n } is a set of variables of the Bayesian Network, P is a set of local probability distributions associated with each variable, X i refers to the variable node and, p a i is the father node of X i .

Many types of software could be used to build the Bayesian network, among them we cite the following [ 62 ]: BayesBuilder, JavaBayes, and Bayes Net Toolbox, which were used in the present study.

3.4. Network Validation

To measure the performance of the obtained Bayesian Network, a ten-fold cross-validation has been conducted. K-fold cross-validation is a powerful means of testing the success rate, accuracy, and robustness of models used for classification [ 63 ]. It consists of randomly partitioning the dataset into ten subsets of equal size; then each subset, in turn, is used to validate the model fitted on the remaining k-1 subsets. The evaluation of the obtained models is performed using the Area Under the ROC (Receiver Operating Characteristic) Curve—named AUC—that plots the following two parameters: True Positive Rate and False Positive Rate that yield the performance of a classification model. The standard scores range from 0 (opposite and wrong prediction) to 0.5 (random prediction) and 1 (perfect prediction).

4.1. Validation of Bayesian Network

The results of the performance evaluation of the learned Bayesian Network ( Figure 1 ) are given in Table 5 .

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Object name is ijerph-18-07155-g001.jpg

Obtained directed acyclic graph corresponding to the study variables.

Validation results of the 10-fold cross-validation.

Variables	Accident Severity		Aberrant Infractions		Speed Infractions		Technology-Based Distraction
	SI/F	M/NI	No	Yes	No	Yes	No	Yes
	0.62	0.62	0.88	0.77	0.90	0.77	0.99	0.93

The AUC score metrics obtained a range from 0.62 to 0.99 for the variables, which confirms the high robustness of the Bayesian Network.

The directed acyclic graph of Figure 1 specifies the joint distribution and represents the dependences/independences between the study variables. Indeed, in the lower right part of the graph, the largest number of study variables such as the zone, gender, vehicle type, and age can be observed in a grouped way. These variables have direct relationships with the infractions’ variables before relating, directly or indirectly, to the severity of the accident. At the top of the graph, the infractions’ variables are directly related to the technology-based distraction variable. Furthermore, aberrant and speed infractions are related to the dummy variable, which is directly related to the severity of the traffic accidents.

4.2. Sensitivity Analysis

4.2.1. assessment of the influence of technology-based distractions on drivers’ infractions.

Estimation of the “a priori” probabilities of drivers’ infractions considering the technology-based distractions is given by the sensitivity analysis results in Table 6 .

Sensitivity analysis for the influence of technology-based distractions on drivers’ infractions.

Technology-Based Distractions	Aberrant Infractions		Speed Infractions
States	No	Yes	No	Yes
No	76.43%	23.57%	92.22%	7.78%
Yes	33.25%	66.75%	85.55%	14.45%

The results in Table 6 show that the probability of committing aberrant infractions increases significantly (from 23.57 to 66.75%) given the fact that the drivers are distracted. Interestingly, these results show that the probability of not committing aberrant infractions increased notably (from 33.25 to 76.43%) when the drivers are not distracted.

Similarly, the sensitivity analysis results in Table 6 show that drivers are more likely to speed when they are distracted. Indeed, the probability increases from 7.78 to 14.45%. Moreover, the probability of respecting the speed limit increases from 85.55% to 92.22% when drivers are not distracted.

The results also show that technology-based distractions are more likely to affect aberrant infractions than speed infractions.

4.2.2. Assessment of the Influence of Technology-Based Distractions on Drivers’ Infractions Considering the Age and Gender of the Drivers

The influence of distracted driving on drivers’ infractions considering the effect of demographics is given by the sensitivity analysis results in Table 7 .

Sensitivity analysis for the influence of technology-based distractions on drivers’ infractions considering the influence of demographics.



Y < 25	No	76.65%	23.35%	85.66%	14.34%
Y < 25	Yes	32.00%	68.00%	72.86%	27.14%
25 ≤ Y ≤ 40	No	76.50%	23.50%	91.39%	8.61%
25 ≤ Y ≤ 40	Yes	33.18%	66.82%	84.10%	15.90%
40 ≤ Y ≤ 60	No	76.61%	23.39%	93.96%	6.04%
40 ≤ Y ≤ 60	Yes	33.82%	66.18%	88.94%	11.06%
Y > 60	No	75.54%	24.46%	95.39%	4.61%
Y > 60	Yes	33.66%	66.34%	91.72%	8.28%


Male	No	76.38%	23.62%	91.45%	8.55%
Male	Yes	33.21%	66.79%	84.25%	15.75%
Female	No	76.56%	23.44%	94.08%	5.92%
Female	Yes	33.45%	66.55%	88.91%	11.09%

According to these results, the probability of not committing aberrant infractions, considering the fact that the drivers are not distracted, increases for drivers younger than 60 years old. Similarly, it has been found that the probability of not speeding, considering the fact that the drivers are not distracted, increases for drivers older than 40 years old and more significantly in the case of elderly drivers (from 92.22 to 95.39%).

With regard to the gender, the results in Table 8 show that the probability of not speeding, considering the fact that the drivers are not distracted, increases notably in the case of females (from 92.22 to 94.08%).

Sensitivity analysis for the influence of technology-based distractions on drivers’ infractions considering the influence of the zone and vehicle type.



Car/Van/All Terrain	No	73.73%	26.27%	92.02%	7.98%
Car/Van/All Terrain	Yes	31.09%	68.91%	85.80%	14.20%
Motorcycles/quads/quadricycles	No	86.58%	13.42%	93.46%	6.54%
Motorcycles/quads/quadricycles	Yes	43.83%	56.17%	84.56%	15.44%
Heavy vehicles	No	78.28%	21.72%	90.44%	9.56%
Heavy vehicles	Yes	39.70%	60.30%	84.73%	15.27%
Other vehicles	No	85.48%	14.52%	95.67%	4.33%
Other vehicles	Yes	34.90%	65.10%	85.37%	14.63%


Road	No	76.19%	23.81%	89.03%	10.97%
Road	Yes	41.06%	58.94%	85.64%	14.36%
Street or similar	No	76.74%	23.26%	96.97%	3.03%
Street or similar	Yes	18.89%	81.11%	85.50%	14.50%
Highway	No	83.82%	16.18%	94.19%	5.81%
Highway	Yes	20.20%	79.80%	60.41%	39.59%

In terms of aberrant infractions, the sensitivity analysis results show that the probabilities increase given the fact that the drivers are distracted (from 66.75 to 68%) in the case of younger drivers (<25 years old). Similarly, it has been found that the probability of speeding for distracted drivers increases, interestingly, in the case of younger drivers (<25 years old) (from 14.45 to 27.14%) and decreases significantly in the case of older drivers (from 14.45 to 8.28%).

4.2.3. Assessment of the Influence of Technology-Based Distractions on Drivers’ Infractions Considering the Zone and Type of the Vehicle

The results of the estimation of the influence of technology-based distractions on drivers’ infractions considering the zone and type of the vehicle are given in Table 8 . The results show that the probability that drivers do not commit aberrant infractions increases in the case of motorcycles, quads, and quadricycles (from 76.43 to 86.58%) and other types of vehicles (from 76.43 to 85.48%), considering the fact that the drivers are not distracted.

In contrast, the results show that the probability of committing aberrant infractions increases more significantly in the case of distracted drivers of cars, vans, and all-terrain vehicles (from 66.75 to 68.91%).

With regard to speed, similarly, the results of the sensitivity analysis show that the probability of respecting the speed limits increases significantly in the case of non-distracted motorcycle riders and drivers of other vehicles (from 92.22 to 93.46%, and from 92.22 to 95.67%, respectively). Moreover, the probability of speeding increases, logically, in the case of distracted motorcycle riders.

The results of the effect of the zone on the behaviors of distracted drivers confirm that distracted drivers commit aberrant infractions on highways and streets (the probabilities increase from 66.75 to 79.80% and from 66.75 to 81.11%, respectively). Nevertheless, distracted drivers are more likely to not respect the speed limit on highways (the probability increases significantly from 14.45 to 39.59%).

4.2.4. Assessment of the Influence of Drivers’ Infractions on Traffic Accident Severity

The severity of traffic accidents due to aberrant infractions or speeding is not always reported in the case of minor traffic accidents. In other words, in such accidents, the police officers are not used to thoroughly fill in the traffic accident report and, most of the time, they do not provide details on whether the drivers involved in minor accidents had committed aberrant infractions or speed infractions.

The influence of drivers’ infractions on the severity of traffic accidents is given by the sensitivity analysis results in Table 9 .

Sensitivity analysis for the influence of the drivers’ infractions on the severity of traffic accidents.

Drivers’ Infractions	Severity of Traffic Accidents

No	90.62%	9.38%
Yes	90.31%	9.69%

No	90.97%	9.03%
Yes	82.11%	17.89%

The results in Table 9 show that the probability that the severity of injuries is serious, resulting in fatalities, increases, given the fact that the drivers committed aberrant infractions (from 9.38 to 9.69%) and more significantly in the case of speeding (the probability increases from 9.03 to 17.89%).

5. Discussions

Traffic accidents have become a major health public issue and preservation of the lives of drivers, passengers and users is among the main concerns of communities around the world. The literature revealed the fact that numerous factors contribute to and influence the occurrence of these accidents and the severity of injuries [ 64 , 65 , 66 ]. Although the continuous efforts of governments and numerous traffic safety policies being issued to control traffic accidents, the rates of disabilities, fatalities, and injuries continue to increase dramatically. Therefore, it has been extensively recommended to analyze the different risk factors influencing traffic accident severity to yield more patterns and polish knowledge to effectively and efficiently prevent road accidents and ameliorate traffic safety.

In this study, we focused, particularly, on the assessment of the influence of technology-based distractions on the performance of drivers behind the wheel. The contribution of this research is twofold. First, it deployed machine learning technique algorithms that, taking advantage of advancements in information technology, allow traffic accidents to be predicted and detect the role of different risk factors in traffic accident scenarios. Second, it overviewed the relationships between the technology-based distractions and the infractions of drivers with full consideration of the impact of gender, age, zone, and the type of vehicle.

Given the crucial aspect of traffic safety, i.e., the prediction of the most important risk factors impacting the occurrence of traffic accidents and influencing the severity of the outcomes, in the present study, a predictive model has been developed to assess the influence of technology-based distractions on the traffic accidents’ severity and unsafe driving behaviors considering the effect of a set of selected risk factors using Bayesian Networks methodology. Indeed, the obtained Bayesian model presented the knowledge in the form of joint probabilities distributions of the study variables, which is vital to make effective and efficient decisions that avoid the drivers’ infractions resulting from technology-based distractions and reduce the severity of injuries to the drivers and passengers.

To conduct this investigation, a recent database over four years (from 2016–2019) on traffic accidents that occurred in Spain was used. According to the sensitivity analyses, the findings indicated that technology-based distracted driving has a significant effect on both the aberrant infractions and speeding. Indeed, the study found that the presence of distractions notably increases the probability of committing aberrant infractions and speeding. This is in line with the literature [ 67 , 68 ] that overviewed the characteristics of distracted driving, which has agreed that new technologies can absorb drivers’ attention and reduce their abilities to judge driving demands and disrespect driving safety requirements that lead, most of the time, to aberrant infractions and unsafe behaviors.

With regard to the risk factors related to the demographics of the drivers involved in traffic accidents, vehicle, and zone, the findings of the present study reported increased probabilities as regards the infractions of distracted young drivers (under 25 years old). These findings coincide with many conclusions of existing studies [ 69 , 70 , 71 ], which had reported similar observations on risky driving behaviors and violations of young drivers who are more susceptible to be involved in fatal crashes due to distraction activities (e.g., mobile phone use, radio, DVD, etc.). In this context, young drivers are inexperienced and easily distracted by interactions with music devices, texting and conversations on mobile phones resulting in slower reaction times [ 72 ].

The sensitivity analysis results of the present study also showed that there are no significant differences in the probabilities of the impact of the gender of drivers on the driving behaviors of distracted drivers.

Furthermore, it has been found that speeding will lead to severe traffic accidents, leading to serious injuries and/or fatalities. These results are tightly associated with the findings of many previous researchers [ 73 , 74 ] that confirmed that speed increases the chance of traffic accidents that result in greater severity.

With regard to the influence of the zone and type of the vehicle factors, the sensitivity analysis showed that distracted drivers are more likely to speed on highways, whereas on streets, distracted drivers are more susceptible to commit aberrant infractions. Moreover, it has been found that the probability of motorcycle riders speeding increases because they are distracted, while car drivers are more likely to commit aberrant infractions. Similar findings reported that mortalities on highways and outside the urban center are mainly due to speed-related crashes [ 75 , 76 ]. Furthermore, an investigation into drivers’ violations found that speeding infractions represent 80% of registered traffic violations on highways [ 77 ].

For future research, it is recommended to extend the current study by considering other risk factors. Such consideration would give wider context and more shreds of evidence on the influence of distracted driving that would help improve and enhance traffic safety more effectively and efficiently.

6. Conclusions

Distracted driving is a growing threat to road safety and accounts for a significant number of serious injuries and deaths in traffic accidents. In this paper, the distracting effects of various technologies on driving performance have been assessed using Bayesian Networks. Unlike other studies, the assessment was then extended to analyze the influence of the driver’s infractions on the severity of traffic accidents. The design of this study considered the driver’s characteristics, the type of vehicle, and zone.

The results of this study documented a strong link between technology-based distracted driving and aberrant and speed infractions. Moreover, the findings showed that these infractions have a direct impact on the severity of traffic accidents. Furthermore, this study specifically found that young drivers are more likely to be distracted.

The deployment of Bayesian Networks yielded a representative graphical structure of the relationships among the technology-based distractions, drivers’ infractions, and traffic accidents’ severity. It contributed to overcoming the observed limitations of most research that has used regression models. Thus, machine learning techniques proved to be more suitable for prediction models in traffic safety problems.

Acknowledgments

We would like to thank the Dirección General de Tráfico from España (DGT) for providing the data for this study.

Author Contributions

Conceptualization, S.G.-H., J.D.F. and W.B.; methodology, J.M.G.; software, J.D.F.; validation, J.D.F. and S.G.-H.; formal analysis, J.D.F. and W.B.; investigation, W.B. and J.D.F.; resources, S.G.-H.; data curation, J.D.F.; writing—original draft preparation, W.B., J.D.F. and S.G.-H.; writing—review and editing, W.B., J.D.F., S.G.-H., M.Á.M.S. and J.M.G.; supervision, S.G.-H. and J.M.G.; project administration, S.G.-H.; funding acquisition, S.G.-H. All authors have read and agreed to the published version of the manuscript.

This research was funded by the project “Modelización mediante técnicas de machine learning de la influencia de las distracciones del conductor en la seguridad vial”, grant number BU300P18, and was funded by FEDER (Fondo Europeo de Desarrollo Regional—Junta de Castilla y León).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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COMMENTS

Texting while driving: A study of 1211 U.S. adults with the Distracted Driving Survey
1. Introduction. Texting and other cell phone use while driving is a major risk factor for motor vehicle collisions and associated injury and death (Wilson & Stimpson, 2010).In 2012, distracted driving was associated with 3300 deaths and 421,000 injuries in collisions in the US; there is evidence that smartphone use is increasingly contributing to these numbers (US Department of Transportation ...
Mechanisms behind distracted driving behavior: The role of age and
The majority of distracted driving research has focused on adolescents or young drivers due to the higher proportion of MVCs compared to the rest of the US population and the higher prevalence of technology use in this demographic. ... Difficulty with executive function was assessed over 75 questions in a pencil and paper format. The ...
Distracted driving: What is the state of the science, and what are our
The Expert Panel members, all of whom are recognized experts in the field of distracted driving research and policy, bring to the table a wide range of expertise. ... paper goes on to compare prevalence and risk of distracted driving using data from studies that use non-naturalistic driving data. The second paper, authored by Thomas Dingus, PhD ...
Distracted Driving and Risk of Road Crashes among Novice and
Estimates based on cell-phone records indicate that cell-phone use among all drivers increases the risk of a crash by a factor of 4. 5,6 Likewise, simulator studies involving adolescent drivers ...
Texting while driving: the development and validation of the distracted
Texting while driving and other cell-phone reading and writing activities are high-risk activities associated with motor vehicle collisions and mortality. This paper describes the development and preliminary evaluation of the Distracted Driving Survey (DDS) and score. Survey questions were developed by a research team using semi-structured interviews, pilot-tested, and evaluated in young ...
Incorporating the Theory of Planned Behaviour into Distracted Driving
Distracted driving is a hot research topic in the field of road traffic safety in recent years. In this study, the core collection of Web of Science is used as the database, and a total of 3884 papers from 2002 to 2022 are obtained. The tendency of published papers is shown in Figure 1.
Detection of Driving Distractions and Their Impacts
Some studies have measured this distance or the headway time and compared baseline conditions with distracted ones [50, 81]. Research studies have found that when drivers are distracted by a mobile phone conversation, texting, manual distractions (infotainment systems), or auditory distractions (audio or music), they leave more space between ...
(PDF) Driver Distraction Detection Methods: A Literature ...
Distracted driving is known to be one of the leading causes of vehicle accidents. ... this work carries out an author-centric literature review of 16 research papers to illustrate the ...
Incorporating the Theory of Planned Behaviour into Distracted Driving
First, a distracted driving scale is constructed based on the theory of planned behaviour (TPB). The questionnaires are distributed in Chongqing, China, and 321 completed questionnaires are obtained. Data are analyzed using mean-variance analysis, one-way ANOVA, T -test, and multivariate test by SPSS 26.0 to determine the significance of ...
Texting while driving: A study of 1211 U.S. adults with the Distracted
The objective of this study was to describe the frequency of cell-phone related distracted driving behaviors. A national, representative, anonymous panel of 1211 United States drivers was recruited in 2015 to complete the Distracted Driving Survey (DDS), an 11-item validated questionnaire examining cell phone reading and writing activities and ...
An evidence-based review: distracted driver
Results: A total of 19 articles were found to merit inclusion as evidence in the evidence-based review. These articles provided evidence regarding the relationship between distracted driving and crashes, cell phone use contributing to automobile accidents, and/or the relationship between driver experience and automobile accidents. (Adjust ...
Effects of Mobile Phone Use on Driving Performance: An Experimental
Finally, several studies used machine learning techniques in road safety studies to address distracted driving issues [31,32]. In this paper, a tree-based machine learning method was deployed for classification and regression problems. The rest of the paper is organized as follows: Section 2 describes the design and methodology of the study.
Distracted Driving Is More Dangerous Than People Realize, New Research
In 2021 more than 3,500 drivers in the U.S. alone died in traffic accidents linked to distracted driving. Using a cell phone is the primary source of distraction, but entering navigational ...
PDF Effectiveness of Distracted Driving Countermeasures
Efectiveness of Distracted Driving Countermeasures: w of the Scientific and Gray LiteraturesMarch 2022According to the National Highway Trafic Safety Administration, 3,142 people were killed in 2019 in motor vehicle crashes in the United States in which one or more drivers was reported as distracted. National Center for Statistics and Analysis ...
Distracted Driving: A Literature Review
Illinois Center for Transportation Series No. 20-005 UILU-ENG-2020-2005 ISSN: 0197-9191. Distracted Driving: A Literature Review. Prepared By. Yan Qi Ravali Vennu Roshan Pokhrel. Southern Illinois University Edwardsville. Research Report No. FHWA-ICT-20-004. A report of the findings of.
Understanding the impacts of mobile phone distraction on driving
1. Introduction. Mobile phone distracted driving (MPDD) is an ongoing challenge for transport network managers. Observational studies conducted in the United States reveal that 31.4% of drivers talk on phone and 16.6% text or dial (Huisingh et al., 2015).Hickman and Hanowski (2012) reported that about 2.2% of commercial motor vehicle drivers were observed using mobile phones while driving.
(PDF) Distracted driving: Prevalence and motivations
Abstract and Figures. This study is a selective review of recent international literature concerning the prevalence of distracted driving and motivations underpinning this behaviour. Distracted ...
Real‐time detection of distracted driving based on deep learning
The final section concludes this paper and discusses some future research directions. 1.2 Related works. In this subsection, we review the existing technologies used to detect distracted driving behaviours, which can be classified based on the sensor modality and detection method. ... Distracted driving detection is performed in real time on an ...
(PDF) Detection of Driving Distractions and Their Impacts
For decades, road crashes have caused many deaths and injuries and generally have had a severe social and economic impact on. societies. According to studies, driver distraction has led to an ...
Texting While Driving: A Literature Review on Driving Simulator Studies
1. Introduction. Road safety is increasingly threatened by distracted driving. One of the highest-risk forms of distracted driving is texting while driving (TWD) [1,2] alongside talking on the phone while driving (TPWD) [3,4].After decades of research, the statistics show that the risks associated with TWD are very high [].According to the United Nations Road Safety statistical data [], car ...
Survey paper Detecting and recognizing driver distraction through
Most importantly, the research efforts in the field could be further subdivided into many subtasks. Thus, this paper aims to provide a comprehensive review of approaches used to detect driving distractions through various methods. We review all recent papers from 2014-2021 and categorized them according to the sensors used.
Distracted Driving Research
Ongoing pilot studies of distracted driving research include: Way to Safety 3.0: The Teen-Parent Study . Teen drivers often tell researchers their parents urge them not to text while driving because it is dangerous, yet the teens frequently see them checking email or texting while driving. In this study, researchers are using a smartphone ...
Ending Distracted Driving is Everyone's Responsibility
New technology in vehicles is causing us to become more distracted behind the wheel than ever before. Fifty-three percent of drivers believe if manufacturers put "infotainment" dashboards and hands-free technology in vehicles, they must be safe. With some state laws focusing on handheld bans, many drivers honestly believe they are making the ...
Texting while driving: A discrete choice experiment
One of the most pernicious forms of distracted driving is texting while driving (TWD) because it involves visual, manual, and cognitive distractions ( Alosco et al., 2012 ). During a simulated driving task, 66 % of drivers exhibited lane excursions while texting ( Rumschlag et al., 2015 ), and in another simulation study, TWD led to five times ...
Brazil Becomes the Largest Nation to Decriminalize Marijuana
Brazil decriminalized marijuana for personal use on Wednesday, making the nation of 203 million the largest to take such a measure and the latest sign of a growing global acceptance of the drug.
Assessment of the Influence of Technology-Based Distracted Driving on
The remainder of this paper is arranged as follows: Section 2 reviews distracted driving; Section 3 sums up the data and methodology of the study; Section 4 provides the results of the study; Section 5 discusses the results and puts forward main findings, limitations, and future research guideline, while Section 6 concludes the paper.