Top PDF Safety Risk of Mobile Phone Use while Driving in Sample of Taxi Drivers

4. DISCUSSION The present study examined the use of mobile phones while driving as a risk factor. The first find- ing was the high rate of mobile phone ownership in the sample. It was also found that mobile phone use (hand-held or hands-free) is a widespread activity among almost all taxi drivers. Work-related reasons may be powerful factors in explaining high levels of phoning while driving. Work-related mobile phone use as a predicted factor while driving has already been highlighted by Troglauer et al. (2006) and Brusque and Alauzet (2008) [8, 21]. The current research found that 81% of the taxi drivers reported that they are using hand-held phone in traffic. This finding is a bit surprising as using a hand-held mobile phone while driving is illegal in Turkey. In other words, this result apparently shows that most of the taxi drivers in Tur- key are currently ignoring the law. This is a common problem for other countries in which most drivers use a hand-held mobile phone while driving [3, 23, 40]. The study identified that one-third of taxi drivers read or write text-messages while driving. Text-messaging rate found by Gras et al. (2007) was 27% among the sample of Spanish university workers [40]. In another study [39], 100.0% of the group reported talking and 72.5% of participants reported text-messaging on a cellular phone while driving at least some of the time. The study also found that disuse of SMS reduces risk of using phone while driving by 80.1%.

The swiftness, or celerity, of punishment associated with offending has been afforded much less attention than those notions of certainty and severity, with an understanding of its importance in deterring traffic offences remaining extremely low. In one of only a small number of studies assessing celerity within traffic offending situations, Nagin and Pogarsky (2001) concluded that it was the least successful of the three components of deterrence theory in predicting offending behaviour. In an earlier study, Yu (1994) found an inconsistency in the effect of celerity, with swift punishment generally successfully able to deter the general public and first time offenders from drink driving, but being less useful in explaining the behaviour of repeat offenders. Thus, those that are more willing to commit an offence on multiple occasions, or may have previously avoided detection, may be less concerned with the swiftness of a punishment than other drivers. As there is little that can be done to manipulate the speed associated with the receipt of punishment of traffic offences due to the necessity of processing before punishment is received, a lack of research in the area is likely to continue. Still, a focus upon the certainty and severity of punishments can be explored in more detail in terms of responses to traffic offending.

INTRODUCTION Teen drivers’ greater willingness to engage in cell phone use while driving relative to other drivers (Tison, Chaudhary et al. 2011), and the widespread adoption of smartphones that allow internet browsing (Neilsen 2013), suggests that cell phone-related driver distraction in this population is likely to remain a source of crash risk (Klauer, Guo et al. 2014). To date, 40 states have passed legislation restricting cell phone use for 16- and 17-year-old drivers, and 14 states have restricted hand-held cell phone use for all drivers (Insurance Institute for Highway Safety 2014).Current approaches to limiting cell phone use while driving include restricting specific behaviors (e.g. texting, hand-held, or any cell phone use) according to the driver age (e.g. younger than 18-years or all drivers) or stage of licensure (e.g. learner permit and intermediate license). However, little is understood about how these restrictions influence teen driver behavior, and which are most effective at reducing cell phone use while driving.

their first study (n = 6,133, mean age= 17.44 years), these authors found that frequencies of participants reported to be a passenger with a driver who engaged in texting while driving were higher than participants self-reporting this behaviour (Tucker et al., 2015). In their second study (n = 4,450, mean age= 15.98 years), Tucker et al. (2015) investigated the explanations of participants of why they would reduce their engagement in texting while driving, and reported that the perceived risk of the behaviour, enforcement of texting bans while driving by Police, experiencing near-crash incidents as a result of texting while driving, and learned crash incidents from texting while driving from others were deterrents to reduce texting while driving. However, no time periods since a near-crash incident occurring and non-use of a mobile phone while driving were specified. In addition, males significantly (p < 0.05) reported to engage in texting while driving more so than females. Across both studies, texting while driving was strongly associated with speeding and talking on the phone while driving (p< 0.001), suggesting that these behaviours happen concurrently (Tucker et al., 2015). Although, these associations could have been found due to generalised risk taking leading to each of the three behaviours rather than the proposed concurrent behaviours. A similar study with a smaller and older sample size was conducted by Gupta, Burns, and Boyd (2016) where the authors conducted a survey with a smaller group of university students (n = 334, mean age = 26 years) in Ohio, USA with the aim of investigating mobile phone use while driving. The authors found positive correlations between the number of text messages sent or received in a typical week while driving (a measure of the levels of engagement in texting while driving), and other risky driving behaviours such as breaches of traffic and non- traffic legal regulations, addictive tendencies (i.e. problematic mobile phone use), as well as affirmative attitudes towards texting while driving (p < 0.05) (Gupta et al., 2016).

Besides the above changes in driving performance that are negative from a traffic safety point of view, some studies found that drivers engage in risk-compensatory behaviour during mobile phone use. The most obvious example is a slower average speed. The possible explanation for this compensatory behaviour could be the drivers’ attempt to reduce performance goals, thus reducing driving task demands and the workload. However, although lowering their performance goals for mobility, drivers still report increased stress and effort. In some cases, a slower mean speed was accompanied by greater variations in speed, which again could be a sign of lowering the performance goal. There is another potential explanation for this behaviour: it could be the result of attention being diverted from driving goals to the phone conversation. Without sufficient attention resources for the primary task of driving, it can be expected that drivers will be less able to cope with emergency

The real-world implications of driver performance studies are uncertain. Drivers may become aware of the risks entailed in using a cellular phone while driving, either through their own intuition and/or through what they learn from family members, friends, or the mass media. Such awareness would be predicted, at least by some behavioral scientists, to induce a change in driving behavior that is aimed at restoring the pre-existing level of driver safety (Peltzman, 1975; Evans, 1991). These theories are rooted in the assumption that the driver cares about his or her own safety. They are not rooted in any altruistic concerns for the safety of other road users, although altruism could also motivate drivers to take some kinds of risk compensation measures. These compensatory behaviors might include maintenance of a constant speed, reduction in passing maneuvers, initiation of calls only when the vehicle is at a stop or is being operated in a remote area, and participation in lengthy and/or intense calls only on certain kinds of trips or in certain kinds of road conditions where there is a perception of relative safety.

Besides visual methods, the interaction at the vehicle-human interface also provides clues to driving information detection. Desai et al., in their work [13], have assumed that the time derivative of force exerted by the driver at the vehicle-human interface, such as pressure on the accelerator pedal, can be used to decide the level of driver alertness. Practically, they have installed a force sensor on the accelerator pedal and collected the exerted force to monitor driver fatigue. In [14], Krajewski et al. have collected steering behavior data and processed them to capture fatigue impaired patterns by using signal processing procedures for feature extraction. They have conducted the experiment with a driving simulator. The automobile manufac- turer Saab has proposed an experimental product AlcoKey [15], which collects a breath sample of drivers before they start the vehicle. Then the AlcoKey’s radio transmitter sends a signal to the vehicle’s electronic control unit to allow it to be started or not based on the alcohol level in the breath sample. These researches use the interactions between human and vehicle to indicate drunk driving. Their systems need to alter the vehicle and be tightly coupled with the auxiliary add-ons, so their compatibility is compromised.

Authors have attributed using mobile phone while driving to many reasons. Age, gender, driving experience, risk perception, attitudes, norms and some other psychological and socio-cultural factors have been mentioned as reasons that rules driver behaviors about mobile phone. Most of studies have performed to identify the affective variables in using mobile phone by drivers based on interview and questionnaire. In this study call answering rate while driving is investigated in a sample of male postgraduate students of a university in Tehran by a driving simulator. We examined the effect of age, driving experience, sensation seeking and educational level in answering rate of participants to mobile phone calls in observational driving scenarios. We wanted to test our hypothesis about the research objectives: answer to mobile phone call while driving is influenced by individual factors.

This section has described the existing data available for mobile-phone-related crashes. In the U.S., the two national databases, FARS and NASS, make use of police crash reports as the basis of their data. The lack of a specific mobile phone use element to these crash reports indicates the probability of underreporting mobile-phone-related accidents. The difficulty in determining accurately whether a mobile phone was in use at the time of the accident means that providing a check box in crash reports does not solve the problem, which has been shown with the analysis of the Oklahoma data. Clearly, it may be very difficult to design a police crash report that provides accurate, unbiased results on mobile phone usage, but this is what would be required to obtain irrefutable knowledge of the extent of the accident risk associated with using a mobile phone while driving. The methods used in Japan, where the required data appear to be available, may need to be considered when designing a data collection system for the U.S. In summarizing the various sources of data available, it appears that most mobile- phone-related crashes occur due to drivers moving from their lane or colliding with a stopped vehicle in their lane, mainly due to inattention to the driving task. These general findings are strikingly similar to the findings of the research studies summarized in Section 4. There is a real need for concise crash data collection to assess the magnitude of the problem and to derive potential solutions. To do this, police crash reports should include a carefully-designed mobile phone use

RCTs of driving assessment should involve a careful design, ran- domising people who drive to either formal testing or usual care and assessment, with longitudinal follow-up of satisfaction with transport (Rosenbloom 2003) and crashes or violations. Ethically, this may not pose a public health hazard in view of some memory clinic studies that suggest no increase in crashes in those with de- mentia (Drachman 1993; Trobe 1996; Carr 2000). Due care will need to be given to advice to participating drivers on informing driver licensing authorities and insurance companies, depending on the jurisdiction(s) within which the study is taking place. The study should also incorporate regular data review by an indepen- dent safety committee. As it is clearly undesirable that all drivers with dementia continue driving indefinitely, the use of a measure of transport efficacy, such as the Life Space Questionnaire (Stalvey 1999). may be a better guide to the primary question posed. It is likely that a close monitoring of the study for adverse events would be the best guide to the potential hazards, as, given that crashes are infrequent events, a study based purely on the secondary objective of safety would have to be very large. Extrapolating from Staplin 2003 where 111 out of 1876 participants in the License Renewal Sample crashed in the 20 months’ follow-up, giving a crash rate of 0.06 per person over 20 months. Using JMP statistical software (SAS Institute) we estimate that a sample size of 5293 in each group would be required to have an 80% power to detect a 20% difference in crash rate between an unselected older driver popu- lation and a dementia group in the same time frame. If, however, we were to choose a larger estimate of increased crash risk such as a 50% difference in crash rates, our required sample size would be 953 per group. For the primary research objective of continued driving, cessation over a 23-month period was almost 50% in a Canadian study of 200 drivers with dementia (Herrmann 2006), giving a withdrawal rate of 0.485 per person over a 23-month pe- riod. This would give us a sample size of 2625 in each group to have an 80% power to detect a 10% difference in driving cessa- tion.

Abstract: Cellphone use while driving has been recognized as a growing and important public health issue by the World Health Organization and U.S. Center for Disease Control and Prevention. Surveys typically collect data on overall texting while driving, but do not differentiate between various forms of cellphone use. This study sought to improve the survey indicators when monitoring cellphone use among young drivers. Experts and young drivers were recruited to propose behavioral indicators (cellphone use while driving behaviors) and consequential indicators (safety consequences of cellphone use while driving) in 2016. Subsequently, experts and young drivers selected the top indicators using the Delphi survey method. We enrolled 22 experts with published articles on cellphone use while driving nationally, and seven young drivers who were freshmen at a state university. Sending a text or e-mail on a handheld phone was picked as the top behavioral indicator by both groups. However, young drivers chose playing music on a handheld phone as the second most important behavioral indicator, which was overlooked by experts. Injury/death and collision were the top two consequential indicators. Experts and young drivers identified the important survey indicators to monitor cellphone use while driving.

Hellinga, 2007; McCartt, Hellinga, & Bratiman, 2006) conducts research in the areas of distracted driving, alcohol-impaired driving, driver safety, young drivers, airbag effectiveness, and occupant restraints. Katherine M. White (White et al., 2010; Nemme & White, 2010; Walsh & White, 2007; Walsh & White, 2006; Walsh et al., 2008; Walsh et al., 2007) investigates road safety, speeding, driver distraction, road-user behavior, and risk factors for cell-phone use while driving. David L. Strayer (Strayer, Drews, & Crouch, 2006; Strayer, Drews, & Johnston, 2003; Strayer & Johnston, 2001; Drews, Pasupathi, & Strayer, 2008) often uses driving simulator in a laboratory setting and examines cell-phone induced driver distraction, the effect of multi-tasking on driving performance, and attitudes of young drivers towards cell-phone use. Anne Bolling’s (Törnros & Bolling, 2006; Törnros & Bolling, 2005) research is in the area of road safety, driver performance in a simulator, effects of cell-phone conversation on mental workload, and hand-held versus hands-free phones. Finally, Paul Atchley (Atchley, Atwood, & Boulton, 2011; Dressel & Atchley, 2008; Nelson et al., 2009) conducts studies on distracted driving, texting among young drivers, and perception of risk in answering and initiating a cell-phone call while driving. In the following, I discuss the findings from existing literature classified into six thematic areas.

The purpose of this study was to investigate the effects of distraction from the use of three types of cell phones while driving: (1) hand-held, (2) portable hands-free, and (3) integrated hands- free. A naturalistic driving study of drivers’ cell phone use was performed. Data was collected from 204 drivers who each took part in the study for 31 days (on average) from February 2011 to November 2011. Only drivers who reported talking on a cell phone while driving at least once per day were recruited. Data acquisition systems in the participants’ own vehicles continuously recorded video of the driver’s face, the roadway, and various kinematic data such as the vehicle speed, acceleration, range and range rate to lead vehicles, steering, and location. A key feature of this study was that participants provided their cell phone records for analysis. This is the first NDS to date that combines call and text records with continuous naturalistic driving data. The cell phone records allowed the determination of when drivers used their cell phone, while the video data allowed the determination of the type of cell phone used, how long it was used for, and what subtasks were executed. The result was a rich data set of driver behavior and

Driver distraction contributes significantly to serious road crashes. Two studies, one in Toronto, Canada [1] and one in Perth, Australia [2] show that crash risk was about four times greater when drivers are interacting with or using mobile related technologies. Some drivers are either unaware or unconcerned to stop using their mobile phone while driving. In Australia, traffic enforcers have gone undercover to spot mobile phone use by drivers and signalling their colleagues ahead of the road to stop them [3]. Furthermore, helmet cameras [4] and roadside speed cameras are being employed to capture drivers using mobile devices. However, this requires significant amount of preparation and policing hours. Traffic enforcers must manually search through hundreds of hours of video to identify the offenders. The detection process is time-consuming, labour intensive and expensive.

An in-depth discussion of negligence and related topics is beyond the scope of this article. With recent legislative trends however, one concept deserves mention: the concept of negligence per se. In short, negligence per se is a doctrine that makes it somewhat easier for a plaintiff to prove a cause of action in negligence. 6 To establish a cause of action for negligence, the plaintiff must show several elements to prove his claim. If, however, there is a statute prohibiting the negligent conduct, such as a law prohibiting the use of cell phones while driving, the doctrine of negligence per se may eliminate the need for plaintiff to prove each of the traditional elements of negligence. 7 Some states have already implemented bans on cell phone use while driving and additional states following suit is likely. 8 The National Traffic Safety Board has even called for a nationwide ban on the non-emergency use of mobile phones while driving. 9 As more of these statutes precluding mobile phone usage by drivers are enacted, there

Several measures such as surveys and in-video footage of vehicles are being adopted to estimate the true prevalence of distracted driving in different countries including the United States (NHTSA, 2010), New Zealand (Sullman and Baas, 2004), and Canada (WHO, 2011) . A major drawback however, is that these methods often utilise self-reported data from drivers and hence rely on driver sincerity (Asbridge et al., 2012). The difficulty in capturing mobile phone involvement increases if a collision occurs. This might be as a result of the challenges associated with in encouraging drivers to admit to mobile phone use, since it is an offence (National Safety Council, 2012) and the interest of the police in recording more obvious violations such as speeding (Asbridge et al., 2012). Ma et al. (2012) noted that police officers are more interested in identifying evidence that can be used for prosecution and since mobile phone use at the exact moment of collision is often considered as a subjective judgement of the police, it is not given due attention.

using a mobile phone while driving (Mark &Peter). And some research showed that male and younger were more likely to use a mobile phone, but there were not gender difference in day (Horberry et al., 2001). There has been an extensive amount of research on the impact of using a mobile phone while driving. Drivers have much slower reactions than nonusers when they keep speaking on a mobile phone or sending a text message while driving, which is thought to increase the risk of crash at least fourfold (Redelmeire and Tibshirani, 1997; Violanti and Marshall, 1996). The observational studies found that using a mobile phone while driving led to driving distraction and it was one of the many distracters. But some research showed that using a mobile phone while driving was not the most frequently observed distractions but eating and drinking were (Stutts et al., 2005). One criticism of these studies is that they were based on self-report than observation of actual behavior.

participant suggestion that traffic law enforcement needs to become stricter on this point [12]. Despite the benefits of using mobile phones in cars, such as making emergency calls and reporting road accidents or serious situations [13], the risk of accident is also increased by up to 4 times even for a short call [2]. In an experimental study about the effect of mobile phone conversations on drivers’ reaction time in braking response, mobile call duration was the most important factor increasing reaction time [14]. Our study showed that most users of mobile phones while driving are middle- aged, with great differences among age groups. This result is different from Pratt et al. (2014) and other studies that found the highest range of mobile use in younger drivers, suggesting a busy lifestyle [15,16]. It also differs from Piner et al. (2010) and Huth et al. (2015), which showed no significant difference by age [2,17]. Regarding driving experience, our study failed to show any relationship between years of driving experience and use of a cell phone. This finding, along with the high overall rate of mobile phone use while driving in Saudi Arabia, suggests that this behavior is socially acceptable in general. Although a high proportion of the population use their mobile phones for a range of activities, phone calls were the most common mobile phone use while driving, and may be particularly distracting [17]. Most respondents used their mobile phones normally, not only in emergencies, and 39.3% did so when driving a moving car in traffic, which is a major safety issue. On the other hand, our study has shown that other activities can also distract the driver and affect his performance, such as eating and drinking. Some studies have shown that visual or manual performance of other activities, such as car radios, also affects driving performance, especially with less experienced drivers [17,18]. The results also supported Solman et al. (2004), which expressed increased awareness of mobile phone use while driving among the population [3].

Matthews et al. (2009) suggested that the context strongly affected mobile phone use, from when users interacted with the phone to what they did with it and for how long. This was supported by findings in this study, with the ‘context’ of the environment having an impact, with some environments consistently being reported as having high phone usage and others much lower. Self-reported phone usage whilst driving was generally far lower than in other environments, the interviews suggested that this was a result of drivers being aware of the safety critical nature of the driving task. The attention required by either the driving or phone tasks was a frequently mentioned prohibiting factor to phone use while driving and was also mentioned far more times as a deterrent to phone interaction than laws prohibiting phone use. This suggests that law enforcement may have comparatively small effect on phone usage while driving, supporting Hill (2004) and McCartt et al.’s (2004) findings that phone use legislation had no significant long term effect on hand-held phone usage levels when they were sampled pre-ban and a year subsequent to the legislation being introduced. The factor which was found most likely to encourage or inhibit self-reported phone usage whilst driving was when there was perceived to be a low

Goodman, et al. (1999) noted a potential causal relation between the increasing number of cell phones and an increasing frequency of cell phone-related car accidents. Supporting this relationship Laberge- Nadeau et al. (2003) found out that, the relative risk of being involved in accidents was 38% higher for cell phone users than for non-users and more than 100% greater for frequent cell phone users. A number of studies have attempted to quantify driver engagement in distracting activities (Strayer &Johnston, 2001; McEvoy et al., 2006). Driver engagement in mobile phone use is common and wide- spread, particularly among drivers who are young, inexperienced, travel in urban areas and have high annual mileage rates (Sullman & Bass, 2004). In order to develop interventions (e.g. media campaigns, enforcement) targeted at those who use mobile phones most often while driving, information is firstly needed about who these drivers are. Unfortunately, paucity of information exists on the use of mobile phones while driving in Nigeria, even in other countries (Horberry et al., 2001).