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Driver engagement in distracting activities and the strategies used to minimise risk
Abstract
This project used an internet survey of 287 Victorian drivers to quantify the extent to which drivers reportedly engage in a range of potentially distracting activities; the factors that influence their willingness to engage; and the strategies they use, if any, to manage distraction. Almost 60% of drivers use a mobile phone while driving and over one third use the phone in hand-held mode. A high proportion of drivers use audio entertainment systems, but relatively few use in-vehicle visual displays such as DVD players. Driver engagement in non-technology-based activities, such as eating, drinking, smoking and reading is also prevalent. Young drivers (18–25 yrs) were significantly more likely to report engaging in certain distracting activities, such as using a mobile phone, CD player and eating and drinking, than their middle-age (26–54 yrs) and older (55+ yrs) counterparts. Most drivers (84%) believe that their driving is less safe when engaged in distracting tasks and take steps to avoid distraction. The survey results provide valuable data to help target distraction policy and countermeasures that build upon the self-regulatory strategies already used by some drivers.
... They experience the car as a space that allows independent personal fulfilment [3]. As a result, a multitude of secondary activities occur in the inside of the car [5][6][7]. This includes the use of mobile devices that significantly contribute to driver distraction [1,8]. ...
... With this solid background on driver distraction, research has shown that young drivers are significantly more likely to engage in driver distraction than older drivers [7,28]. Their association with distraction can be attributed to the age related factors that occur throughout the transition to adulthood. ...
... For that reason, driver distraction has become an increasing concern among policymakers, further re-enforced by the proliferation of the smartphone in recent years [8,72]. Young people show the highest affinity to perform distracting activities throughout a trip, in large part due to their underdeveloped driving and hazard perception skills [7,28,31]. This can be attributed to age related factors including their psychosocial development that take place along their transition to adulthood [4,73]. ...
We are facing an increase in the emergence of distracting activities while driving. This is especially the case for young people who, more than other age groups, employ their cars as a place of personal fulfilment. This study proposes an interdisciplinary safe-by-design (SbD) heuristic to address this emerging risk. It harnesses a German version of the Behaviour of Young Novice Driver Scale (BYNDS) to gather representative information about young people's distracting activities. This information is then used to address to limitations of Driver Monitoring Systems (DMS) and posit safety measures in the context of young driver distraction. Our novel approach reveals three recommendations that should guide the employment of DMS in future generations of cars. We argue that the sole use of DMS Type 1 (i.e. vehicle motion data) is not sufficient to cope with the complex range of distracting activities that occur inside the car. We suggest designers and technologists employ DMS Type 2 (i.e. cameras and acoustic sensors) as this makes it possible to capture rich information about humans, objects and their interaction. In light of concerns about data privacy, policymakers must act to regulate the ethical use of data from the inside of the car and to find the necessary trade-off between data privacy and the unnecessary attrition of young human lives. This research provides a reasonable foundation for this discussion.
... Nonetheless, 2 decades later, not much is known about everyday music engagement while driving. Many studies have simply reported levels of frequency; these vary between a modest 12% incidence (North et al., 2004) to a significant 94% incidence (Young & Lenne, 2010). Every so often, commercial surveys flood the Internet and social media, with headlines linking specific music genres to personality traits and/or affective temperaments among drivers. ...
... Unlike previous findings (Stutts et al., 2003(Stutts et al., , 2005Young & Lenne, 2010), the respondents did not report to continuously change radio stations, swap between CDs, or scroll through playlists while driving. Similar to findings reported by Dibben and Williamson (2007), the majority (61%) of respondents claimed to listen to the same music tracks in the car as they usually listen to at home (i.e., 74% reported to listen to pop music). ...
... Unlike previously published research studies (Stutts et al., 2003(Stutts et al., , 2005Young & Lenne, 2010), the young-adult driver respondents did not constantly engage in manual manipulation of the music equipment while they drive; they did not report turning on/off the radio, toggling channel knobs/buttons, adjusting volume pans, flipping cassette tapes, swapping CDs, or thumb-scrolling through mp3 playlists. Perhaps this formerly reported music behavior echoes technologies of yesteryear that have since eclipsed with the advancement of wireless Bluetooth linking smartphones to the vehicle's entertainment center. ...
Not much is known about young-adults’ everyday behaviors involving music while driving. To widen the inquiry, Slor and Brodsky (Slor, 2019) developed the In-Cabin Music Engagement Questionnaire (iCMEQ). The purpose of the current study was to solicit information about the use of music based on drive types, driving scenarios, driver behaviors and affective dispositions, as well as drivers’ beliefs about in-cabin music. Finally, the In-Cabin Music Engagement Questionnaire highlights the imaginary enactment of a music performance by drivers while otherwise engaged in driving on the road. A total of 140 young-adult drivers in Israel completed this survey. The findings show that all respondents listened to music while driving a car; that they preplan playlists based on the driving conditions they expect to encounter; and they use music to self-regulate affect and mood while on the road. Social media has exposed young-adult drivers to conflicting messages about the effects of music on driver behavior, and, subsequently, they demonstrate great uncertainty about the effects of music engagement on driver concentration and vehicular control. As a result, young drivers may be more at risk by engaging in music than they perceive.
... Using a mobile phone while driving has a direct impact on the driver's actions, as well as on the performance of their driving task (Atwood et al., 2018;Backer-Grøndahl and Sagberg, 2011;Kubo, Noguti and Bastos Volume 33 | e3053 | 2025 Bastos et al., 2020;Christoph, Wesseling and van Nes, 2019;Morgenstern, Schott and Krems, 2020;Oviedo-Trespalacios et al., 2018;Phuksuksakul, Kanitpong and Chantranuwathana, 2021;Schneidereit et al., 2017;Wijayaratna et al., 2019;Young and Lenné, 2010). This type of secondary task can lead to attention diversion and manual-visual distraction, which affects performance due to the repetitive shift in focus, the physical constraints of handling the device, and the redirection of the visual field within the vehicle (Atwood et al., 2018). ...
... This type of secondary task can lead to attention diversion and manual-visual distraction, which affects performance due to the repetitive shift in focus, the physical constraints of handling the device, and the redirection of the visual field within the vehicle (Atwood et al., 2018). Mobile phone use (MPU) while driving is currently recognized as one of the most dangerous road distractions (Young and Lenné, 2010). Brazilian Traffic Law (Law No. 9,503 of September 23, 1997, Article 252, as amended by Law No. 13,281 of 2016 classifies driving with one hand as a serious infraction, except when signaling, changing gears, or activating vehicle equipment. ...
O uso do telefone celular ao volante é fator de risco reconhecido para a ocorrência de sinistros de trânsito. Pouco ainda se conhece sobre as características de uso do telefone celular como tarefa secundária à condução no Brasil. O objetivo deste estudo foi produzir e analisar indicadores de desempenho da segurança viária relacionados ao uso do telefone celular ao volante a partir de uma base de dados naturalísticos de direção. A metodologia consistiu em um estudo observacional com a análise de vídeos obtidos a partir do monitoramento da atividade real de condução de 32 condutores em Curitiba e Região Metropolitana. O uso mais comum foi para verificar/navegar – 44,96% dos usos. A frequência média de uso foi de 8,71 usos/h e a duração de 55,34 segundos por uso. Em média, os condutores reduziram a velocidade em 6,32 km/h após o início do uso e aumentaram em 5,11 km/h após a conclusão. Verificar/navegar foi o tipo de uso com maior adaptação de velocidade, apresentando uma redução média de 7,39 km/h ao iniciar o uso e um aumento médio de 3,55 km/h ao fim do uso. Em conclusão, a adaptação da velocidade para o uso do telefone celular foi relacionada à complexidade da atividade, conforme os níveis de demanda manual, visual e cognitiva. No entanto, os condutores não perceberam o acréscimo de risco nas ligações ou envio de mensagens de voz, evidenciado a necessidade de medidas mais efetivas para reduzir o engajamento na tarefa secundária de uso do telefone celular ao volante.
... Although drivers adjust their multitasking in response to traffic conditions, driving situations, in-car interface design, and automation levels, their ability to adapt effectively depends on their certainty about the changing task environment [69,51,47,46,62,13,42]. To improve road safety and design less distracting In-Vehicle Information Systems (IVISs), a deeper understanding and modeling of drivers' multitasking behavior and adaptation to task environment changes are essential. ...
... Driving, despite its long history, is undergoing a major transformation due to advanced in-car interactions and the spread of (partially) automated driving features. The safety implications of these technologies depend on our ability to understand human cognition and its adaptability to new systems [19,11,33,1,49,69]. In this paper, we develop a computational cognitive model of driver multitasking and argue that such modeling is essential for designing safety-critical systems. ...
Modern driving involves interactive technologies that can divert attention, increasing the risk of accidents. This paper presents a computational cognitive model that simulates human multitasking while driving. Based on optimal supervisory control theory, the model predicts how multitasking adapts to variations in driving demands, interactive tasks, and automation levels. Unlike previous models, it accounts for context-dependent multitasking across different degrees of driving automation. The model predicts longer in-car glances on straight roads and shorter glances during curves. It also anticipates increased glance durations with driver aids such as lane-centering assistance and their interaction with environmental demands. Validated against two empirical datasets, the model offers insights into driver multitasking amid evolving in-car technologies and automation.
... In addition, a surveybased study asked the participants to rate perceived crash risk due to various secondary tasks into five categories: extremely risky to no risk at all (K. L. Young & Lenné, 2010). This study found manipulating portable music system, manipulating in-vehicle entertainment system, and manipulating vehicle controls were considered moderate to little risky secondary tasks by most of the participants. ...
... This study found talking to passengers is the one of the least risky rated secondary task (K. L. Young & Lenné, 2010). Another study was conducted to understand the driver's risk perception of various secondary tasks where they have to rate dangers associated with tasks from 1 (least risky) to 10 (most risky) (Rupp et al., 2016). ...
This research explores the relationship between driver distraction crash, near-crash, risk using the naturalistic driving data from the second Strategic Highway Research Program (SHRP 2). The objectives of this study are to assess the risk of crash and near-crash events under different contextual environments based upon whether the driver was engaged in any secondary (i.e., non-driving related) tasks. The research also compares speed profiles of distracted drivers in low-speed and high-speed environments, providing important insights into how driver behavior changes based upon the type and intensity of distraction.
In general, the analysis revealed the risks are more pronounced for those tasks that are a combination of visual and manual distractions or require longer gaze, such as cell phone talking, reaching or manipulating an object, adjusting center stack control and external distractions. Besides this, roadway infrastructure and environment also played a key role in the occurrence of safety critical events and speed selection.
Ultimately, the results of this study provides further motivation for more aggressive legislation and enforcement against distracted driving. This can be achieved by enforcing strict laws and fines, graduated licensing process, public campaigns, modified infrastructure (rumble strips and tactile lane marking), and other such measures.
... The city roads drivers are found least engaged with their smartphones as compared to drivers on the highway [37]. Also, the frequency of smartphone activities while driving varies with different driving speeds [38]. ...
... It is estimated that in the UK, 21% of drivers have used their smartphone while driving at least once in the previous month [88]. It is estimated that 27% to 70% of drivers are using their mobile phones while driving depending on age [37]. ...
... By selfregulating their driving and/or smartphone behaviors, drivers believe they can compensate for risks (Zhou et al., 2016) and reduce the likelihood of detection by police Oviedo-Trespalacios et al., 2018). Studies have shown the decision to engage in smartphone tasks, and choice of self-regulatory strategy, is conditional and adapted to three factors: driver characteristics, road traffic conditions, and task demands (Hancox et al., 2013;Oviedo-Trespalacios et al., 2018, 2019Tivesten & Dozza, 2015;Young & Lenné, 2010). Driver characteristics such as crash risk perception (the appetite and interpretation of risk; Oviedo-Trespalacios, and confidence in multi-tasking or driving (Hancox et al., 2013) can determine engagement and choice of behavioral adjustments. ...
... Indeed, waiting at an intersection was viewed as an innocuous and acceptable moment to engage with smartphones. Alternatively, participants reported avoiding use in situations that required greater concentration such as near schools, at high speeds, in the city, or on windy roads (Oviedo-Trespalacios, Haque, et al., 2017a;Young & Lenné, 2010). The following exchange between Stuart and Lola, both heavy users while driving, demonstrate this: Stuart [P1]: There are times when it seems as if you're more safe to use your phone when you're driving. ...
Introduction:
Young drivers are the most vulnerable road users and most likely to use a smartphone illegally while driving. Although when compared with drink-driving, attitudes to illegal smartphone risk are nearly identical, smartphone use among young drivers continues to increase.
Method:
Four in-depth focus groups were conducted with 13 young (18-25 years) drivers to gain insight into their perceptions of the risks associated with the behavior. Our aim was to determine how drivers navigate that risk and if their behavior shapes and informs perceptions of norms.
Results:
Three key themes emerged: (a) participants perceived illegal smartphone use as commonplace, easy, and benign; (b) self-regulatory behaviors that compensate for risk are pervasive among illegal smartphone users; and (c) risk-compensation strategies rationalize risks and perceived norms, reducing the seriousness of transgression when compared with drink-driving. Young drivers rationalized their own use by comparing their selfregulatory smartphone and driving skills with those of "bad drivers," not law abiders. Practical Applications: These findings suggest that smartphone behaviors shape attitudes to risk, highlighting the importance for any countermeasure aimed at reducing illegal use to acknowledge how a young person's continued engagement in illegal smartphone use is justified by the dynamic composition of use, risk assessment and the perceived norms.
... This survey reports detailed information on knowledge, attitudes, and behavior regarding the use of mobile phones while driving in a metropolitan area. Our findings show that there is no correlation between mobile phone use while driving and sex of the interviewed, in agreement with several studies (Tontodonato and Drinkard, 2020;Townsend, 2006;Young and Lenné, 2010). The previous research is not homogeneous on the role of sex and cell phone use while driving: some studies found no correlation while others did find it. ...
... Considering the elements of the sample, age distribution ranges from 18 to 90 years, with an average of 39, which made it possible to investigate the correlation between age and the three main focus of this research: knowledge, attitudes and behavior (Table 5, Model I, II and III). A correlation was observed between age respectively with attitude and behavior, and it was observed that it was a positive one: in fact, the older the responders, the better the attitudes and behavior, which is in agreement with a previous study in which was demonstrated that it was in fact young drivers who are more frequently involved in risky behaviors and traffic accidents compared to other age groups (Young et al., 2010). Other studies also confirm this data, suggesting that increasing age was associated with lower mobile phone use (Arvin et al., 2017;Truong et al., 2016 and2019): this is expected since younger generations arguably are more familiar with mobile devices (Arvin et al., 2017;Atchley et al., 2011). ...
The use of mobile phones while driving is one of the main causes of road accidents and it is a phenomenon in continuous growth. The key aim of this study is to analyse simultaneously knowledge, attitudes, and behavior toward the use of mobile phones while driving in one of the largest and populous metropolitan areas of Italy, Naples. The data acquired from 774 questionnaires - administered to subjects evenly divided by gender and with an average age of 39 years - revealed that 69 % have used their mobile phone while driving at least once in their lifetime. Among those who used the phone, 63.6 % use it to make phone calls while 75.2 % only to answer them; 49.1 % read messages and only 33.3 % write them. It is also notable that 34.1 % do not stop to answer a call and only 10 % do not value the use of headsets while driving as fundamental. The results indicate that cell phone usage while driving is common in the study population, despite many having university-level education and satisfactory risks awareness. The multiple linear regression analysis shows how knowledge is not correlated to the behavior held. On the contrary, attitudes are strongly correlated to knowledge and behavior, meaning that good attitudes bring forth positive behavior. According to the collected data and statistical analysis, it is possible to identify factors that can greatly affect the use of mobile phone while driving and establish targeted prevention programs.
... Previous surveys have looked at similar features, such as the National Survey on Distracted Driving Attitudes and Behaviors (Schroeder et al., 2018), which largely looked at the prevalence and motivations behind distracted behaviors. Likewise, Young & Lenné (2010) considered prevalence, age, and general attitudes. A 2019 trial of road-side automated device detection cameras in New South Wales, Australia, identified the hand used, task involved, presence of others, and vehicle speed (Faulks, 2020), but did not identify phone-height (the height at which drivers hold their phone) or driver age. ...
... Reported increases in prevalence across age are not surprising, and are in line with previous research (Funkhouser & Sayer, 2012;Schroeder et al., 2018;Young & Lenné, 2010). ...
Objective
Research shows frequent mobile phone use in vehicles but says little regarding how drivers hold their phone. This knowledge would inform countermeasures and benefit law enforcement in detecting phone use.
Methods
934 participants were surveyed over phone-use prevalence, handedness, traffic-direction, and where they held their device.
Results
The majority (66%) reported using their phone while driving. Younger drivers were more likely to use their device. Of device-users, 67% preferred their passenger-side hand, 25% driver-side, and 8% both. Height- wise: 22% held in-lap, 52% even with the wheel, and 22% at wheel-top. Older drivers were more likely to hold the phone in the highest position The three most popular combinations were passenger-middle (35%), passenger-low (19%), and passenger-high (13.9%). There was insufficient evidence of differences based on handedness, prevalence, or traffic-direction.
Conclusion
Driver-preferred attention regions often require substantial neck flexion and eye-movement, which facilitates distraction detection. However, behavior may change in response to future interventions.
... Regarding distracted driving, mobile phone use emerges as another critical factor contributing to road traffic crashes (Atchley et al., 2012;Burdett et al., 2019;Stavrinos et al., 2009). Even secondary tasks, such as interacting with in-vehicle technologies, can be a distraction source leading to traffic crashes (Young & Lenné, 2010). Additionally, a driver's personality and sociodemographic characteristics can significantly influence the driving behavior and the likelihood of crashes s (Tinella, Caffò, et al., 2021). ...
This thesis explores how cognitive functions can improve road safety, emphasizing the limitations of current traffic education programs that focus on theoretical knowledge and basic vehicle handling, underlining the importance of cognitive skills and the need for a multidomain approach. The research proposes practical cognitive training methods to predict and enhance safe driving, seeking to reduce the likelihood of accidents. The work includes various experiments examining the relationship between cognitive capacity and traffic infraction rates, as well as evaluating driving performance and the effectiveness of cognitive training in improving road safety. The thesis concludes by highlighting the significance of cognition in driving ability and analyzes the effectiveness of cognitive training programs.
... Furthermore, the study focuses on teenagers and young adults as individuals under the age of 30 are most susceptible to problems with concentration while driving due to performing a secondary task (like talking/singing, cell texting, reaching for in-vehicle objects etc.) [26]. Research has shown that individuals in the 18-25 age group are more likely to engage in additional activities while driving compared to those in the 26-54 and 55+ age groups, and are also the most susceptible to car accidents [27]. Distraction, in turn, can lead to an increase in reaction time to appearing stimuli ...
The study aimed to evaluate the effect of two different types of music and cognitive task on students’ reaction time and additionally to compare results of two groups: drivers and non-drivers. Reaction tests associated with driving-related skills were conducted on 52 students aged 18–25, 33 of them were drivers and 19 were no-drivers. Tests included three parameters: choice reaction time, the number of correct reactions and the number of no reactions and were conducted in four conditions: in silence (control test), with energetic music, with relaxing music and with cognitive task (answering questions). It turned out that results of all three parameters were better in silence and with music than while doing cognitive task. There were no differences between the two examined groups.
... 3,4 Although in-car listening may seem trivial, it is evident that for 72%-100% of drivers, it has become an essential part of the driving experience. 5 Since the turn of the millennium, the automobile has been the most preferred setting for listening to music. 6,7 In fact, 75% of the time drivers spend behind the wheel, they listen to music. ...
Background
Many studies have assessed the effect of music on driving. However, their results are very scattered and contradictory. Therefore, this systematic review is conducted to determine the effect of music on driving performance and drivers’ physiological and psychological indicators.
Methods
Scopus, PubMed, and Web of Science databases were searched until July 2023. A manual search in Google Scholar for gray literature was conducted. The Simulation Research Rubric (SRR) tool was used to assess the reporting quality of the studies. Stata software (StataCorp, version 16) was used to perform a meta-analysis.
Results
A total of 2650 records were identified. The findings of 19 studies were analyzed. Most of them were carried out in high-income countries (HICs) using simulators. The most frequently used music style was classic rock. The meta-analysis results indicated that music with high and medium volume increases the average driving speed, and music with low volume decreases it. Although music in every mood reduces the average reaction time, it positively reduces response delay and increases coherence. Music with high volume decreases the heart rate, but music with medium and low volume increases it. Listening to music increases the level of arousal and mental load.
Conclusion
It was concluded that, in some indicators, listening to music has adverse effects on driving. However, in many indicators, music has a positive impact on improving driving safety. It is better to choose appropriate music for different driving conditions and to train the drivers about it.
... Regarding distracted driving, mobile phone use emerges as another critical factor contributing to road traffic crashes [52][53][54]. Even secondary tasks, such as interacting with in-vehicle technologies, can be a distraction source leading to traffic crashes [55]. Additionally, a driver's personality and sociodemographic characteristics can significantly influence the driving behavior and the likelihood of crashes [56]. ...
Driving is a critical aspect of personal mobility and autonomy, but ensuring road safety requires a comprehensive evaluation of driving abilities beyond self-reported behaviors and practical skills. This article emphasizes the importance of cognitive assessment in determining fitness to drive and explores the potential benefits of using digital tools for such evaluations to enhance road safety. Implementing these digital tools does come with challenges, such as unfamiliarity with digital cognitive reviews for some and the requirement of adaptability to evaluate cognitive skills across various age demographics. Additionally, the absence of standardization in driving assessments across different regions can result in inconsistencies in judging who is fit to drive. Despite these hurdles, integrating digital cognitive evaluations and training into conducting assessments and educational initiatives can more effectively comprehend and address mental aspects of driving, thereby potentially reducing crash risk and promoting road safety. This hypothesis-driven approach proposes that a thorough assessment of an individual's readiness to drive, focusing on vital cognitive domains associated with safe driving, can contribute to safer roads and yield substantial social, economic, and personal benefits. We encourage future research and educators to consider these insights when developing driving education programs and assessments of driving fitness.
... Another study conducted by Pettitt et al. [42] provided a more comprehensive list of distraction factors and categorized them into 3 groups: external sources, internal sources (technology-based), and internal sources (non-technology-based). Young and Lenné [43] provided a list of individual risky activities without categorizing them. Given that there are a variety of specifc distracting factors that have been implemented in the reviewed studies' experiments, it is essential that they are categorized to make the identifcation and comparison of studies and their outcomes possible. ...
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 increase in driving-related risks. In recent years, there have been more distracting factors that commonly affect drivers, highlighting the need for a resolution. Therefore, as technology is becoming more advanced, there is an opportunity to minimize these risks, for which driver distraction detection would be required. As there are a variety of distractions that might affect drivers and their performance, there are many studies focusing on this topic. To better understand the field of driver distraction detection, this paper has reviewed the existing studies in this field. For this purpose, different variables of the existing methodologies and experimental setups are identified and explained. Also, the results of these experiments and the impacts of different distraction factors on drivers’ physiological responses, visual signals, or their performances are categorized and described. Furthermore, this study discusses the factors of the existing methodologies and their results, along with pointing out the research gaps. The purpose of this study is to assist future research and investigation in this field, by creating a review that comprehensively covers different aspects of existing studies and discusses and assesses their methodologies and findings.
... These decisions are often constant over a trip. Some drivers, for example, report that they never engage in a secondary task in heavy traffic, in poor weather conditions, or when driving at nighttime (Young & Lenné, 2010). Oviedo-Trespalacios et al. (2019) modeled strategic self-regulation as the decision to pull over to perform a secondary task. ...
Driver assistance systems are designed to increase comfort and safety by automating parts of the driving task. At the same time, modern in-vehicle information systems with large touchscreens provide the driver with numerous options for entertainment, information, or communication, and are a potential source of distraction. However, little is known about how driving automation affects how drivers interact with the center stack touchscreen, i.e., how drivers self-regulate their behavior in response to different levels of driving automation. To investigate this, we apply multilevel models to a real-world driving dataset consisting of 31,378 sequences. Our results show significant differences in drivers' interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. Specifically, at higher levels of driving automation (level 2), the mean glance duration toward the center stack touchscreen increases by 36% and the mean number of interactions per sequence increases by 17% compared to manual driving. Furthermore, partially automated driving has a strong impact on the use of more complex UI elements (e.g., maps) and touch gestures (e.g., multitouch). We also show that the effect of driving automation on drivers' self-regulation is greater than that of vehicle speed and road curvature. The derived knowledge can inform the design and evaluation of touch-based infotainment systems and the development of context-aware driver monitoring systems.
... As above-mentioned, the mobile phone use is more 104 intense and frequent in urban roads and therefore this experiment is taking place in an urban 105 environment. Additionally, it is focused on young drivers (18-33 years old), as they are more familiar 106 and prone to be distracted by smartphone applications (Young et al., 2010), while older drivers often 107 self-regulate by avoiding using mobile phones while driving (Donorfio et al., 2008). 108 ...
... Drivers would also self-regulate their mobile phone use behaviors at three levels: strategic level (e.g., whether or not to use a mobile phone while driving) (Oviedo-Trespalacios et al. 2017), tactical level (e.g., when to use the mobile phone while driving) (Christoph et al. 2019), and operational level (e.g., how to use the mobile phone while driving) (Oviedo-Trespalacios et al. 2019). On the operational level, the strategies of controlling the conversation time (Young and Lenn e 2010), shortening the message (O'Brien et al. 2010), reminding the caller that they were driving and regulating the phone use modes (Prat et al. 2017) have always been adopted by distracted drivers (Zhou et al. 2012;Zhou et al. 2016). Through questionnaire survey, these studies suggested that distracted drivers can use phone-use-related self-regulatory behaviors to compensate for the detriment. ...
Objective:
Drivers usually appear to self-regulate their driving behaviors in situations considered to be challenging, such as mobile phone-distracted driving. It is important to clarify how drivers self-regulate their actual behaviors. In addition, few studies investigated driver distraction in active and responsive scenarios. Therefore, the present study aimed to gain a better understanding of drivers' actual self-regulation of driving behaviors and phone use behaviors while mobile phone-distracted driving in active and responsive scenarios. The contribution of compensatory beliefs to self-regulation was also explored.
Methods:
This study was conducted using a 2 (mobile phone use behaviors: phone calling vs. WeChat messaging) × 2 (scenarios: active vs. responsive) within-group design. A total of 34 participants completed a driving simulation experiment. The dependent variables of drivers' driving behaviors, phone use behaviors, and physiological data were collected. Participants' compensatory belief was also measured.
Results:
The results showed that the speed reduction in the stages with WeChat messaging was significantly greater than that in the stages with phone calls, and the speed reduction in the responsive scenario was significantly greater than that in the active scenario. Participants would adopt relatively equal phone-use-related self-regulatory behaviors in active and responsive scenarios. Participants with higher compensatory beliefs had relatively greater speed reduction in most scenarios, but fewer phone-use-related self-regulatory behaviors. In addition, the respiratory rate could contribute to evaluating the changes in drivers' physiological status during phone calling-distracted driving.
Conclusions:
Participants would self-regulate driving behaviors and phone use behaviors according to different distracted driving tasks and scenarios. The driving-related self-regulation in WeChat messaging scenarios and responsive scenarios was greater. There was a trend in the effect of compensatory beliefs on actual self-regulatory behaviors, which needs to be further verified in the future. This study contributes to the verification of the different actual driving-related and phone-use-related self-regulatory behavior of drivers in active and responsive mobile phone distracted driving scenarios.
... Strategic self-regulation describes driver decisions that are made on a timescale of minutes or more [45] and are often constant over a trip. Young and Lenné, for example, report that some drivers state that they never engage in a secondary task in heavy traffic, in poor weather conditions, or when driving at nighttime [54]. Oviedo-Trespalacios et al. modeled strategic self-regulation as the decision to pull over to perform a secondary task [41]. ...
... To identify the factors that cause or contribute to distractions, researchers have undertaken many self-report surveys and interviews, which identified actions commonly performed by drivers during driving (National Traffic Law Center, 2017;Pope et al., 2017;Schroeder et al., 2018;Stavrinos et al., 2020;Young and Lenné, 2010). However, selfreporting bias was a limitation of survey studies (af Wåhlberg, 2011). ...
Distracted driving is a major traffic safety concern in the USA. To observe and detect distracted-driving events, various methods (e.g., surveys, videos, and simulations) involving the collection of cross-sectional data from individual subjects have been used in the transportation field. In this study, we employed an unconventional approach of on-road observations using a moving vehicle to collect data on distracted-driving events for multiple subjects in New Jersey. A data-collection crew member continuously navigated selected corridors to record driver-distraction events. A GPS (Global Positioning System) tracker was used to timestamp and record the location of each incident. Two non-parametric tests (Mann–Whitney U test and Kruskal–Wallis test) were performed to identify the significance of the variations in distracted-driving behaviors due to changes in temporal variables (e.g., day of the week, season), the type of roadway, and the geometric properties of the roadway. The results indicated that cellphone use was the leading type of distraction. Additionally, “handheld phone use (phone to ear),” “fidgeting/grooming,” “drinking/eating/smoking,” and “talking to passengers” events were significantly affected by the time of day and the geometric properties of the roadway. The results of this study are expected to assist state and local agencies in promoting awareness of distracted driving with the aim of reducing the frequency and severity of distracted driving-related crashes.
... The bus driver and bus aides are the key personnel within the child's immediate environment of the school bus. Driver distraction can be broadly understood as a trigger that draws one's attention from the task of driving (Young & Lenné, 2010). The risk of distraction could be considered particularly significant in this domain where drivers are compelled, as part of their job, to perform additional secondary tasks (e.g. ...
School buses facilitate access to education for many children. This research aimed to systematically review factors associated with safe school bus transportation for children with neurodevelopmental disorders (NDDs). Searches of 5 databases, combining terms denoting NDDs and school buses, for English publications since 2000, yielded only 12 relevant articles among 1524 records. Literature was limited to parent-based studies, guidelines, reviews or commentaries. There was scant attention to the immediate roles of bus drivers and aides. Literature recommendations included increased attention to the needs of children with NDDs and improved communication, collaboration, support and training across all key stakeholders, particularly to improve implementation of individual child safety plans. Further research is needed on this critical support service for many families.
... Another study conducted by Pettitt et al. [26], provided a more comprehensive list of distraction factors and categorized them into 3 groups: external sources, internal sources (technology-based), and internal sources (non-technology based). Young and Lenné [27] provided a list of individual risky activities without categorizing them. Given that there is a variety of specific distracting factors that have been implemented in the reviewed studies' experiments, it is essential that they are categorized to make the identification and comparison of studies and their outcomes possible. ...
... Strategic self-regulation describes driver decisions that are made on a timescale of minutes or more [45] and are often constant over a trip. Young and Lenné, for example, report that some drivers state that they never engage in a secondary task in heavy traffic, in poor weather conditions, or when driving at nighttime [54]. Oviedo-Trespalacios et al. modeled strategic self-regulation as the decision to pull over to perform a secondary task [41]. ...
With ever-improving driver assistance systems and large touchscreens becoming the main in-vehicle interface, drivers are more tempted than ever to engage in distracting non-driving-related tasks. However, little research exists on how driving automation affects drivers' self-regulation when interacting with center stack touchscreens. To investigate this, we employ multilevel models on a real-world driving dataset consisting of 10,139 sequences. Our results show significant differences in drivers' interaction and glance behavior in response to varying levels of driving automation, vehicle speed, and road curvature. During partially automated driving, drivers are not only more likely to engage in secondary touchscreen tasks, but their mean glance duration toward the touchscreen also increases by 12% (Level 1) and 20% (Level 2) compared to manual driving. We further show that the effect of driving automation on drivers' self-regulation is larger than that of vehicle speed and road curvature. The derived knowledge can facilitate the safety evaluation of infotainment systems and the development of context-aware driver monitoring systems.
... Special focus should be given since rural roads are the road type with the most fatalities, due to the majority of road fatalities occurred on this type of road in most countries (IRTAD, 2020). This is the first time that these applications are tested on young drivers exclusively, who are more prone to be distracted (Young and Lenné, 2010), and present increased accident risk compared to more experienced drivers, and the risk increases when they are distracted by phone use (SWOV, 2021). The study results apply to rural road environments. ...
The present study aims to investigate the impact of texting and web surfing on the driving behavior and safety of young drivers on rural roads. For this purpose, driving data were gathered through a driving simulator experiment with 37 young drivers. Additionally, a survey was conducted to collect their demographic characteristics and driving behavior preferences. During the experiment, the drivers were distracted using contemporary smartphone internet applications i.e., Facebook Messenger, Facebook and Google Maps. Regression analysis models were developed in order to identify and investigate the effect of distraction on accident probability, speed deviation, headway distance, as well as lateral distance deviation. Additionally, random forest (RF), a machine learning classification algorithm, was deployed for real-time distraction prediction. It was revealed that distraction due to web surfing and texting leads to a statistically significant increase in accident probability, headway distance and lateral distance deviation by 32%, 27% and 6%, respectively. Moreover, the driving speed deviation was reduced by 47% during distraction. Apart from the real-time prediction, the RF revealed that headway distance, lateral distance, and traffic volume were important features. The RF outcomes revealed consistency with regression analysis and drivers during the distractive task are more defensive by driving at the edge of the road near the hard shoulder and maintaining longer headways. Overall, driving behavior and safety among young drivers were both significantly affected by the investigated internet applications.
... Numerous researchers have focused on cellphone use while driving (Dingus, et al., 2016;Beanland, Fitzharris, Young, & Lenné, 2013;Caird, Willness, Steel, & Scialfa, 2008;Dingus, et al., 2006;Jashami, Abadi, & Hurwitz, 2017;McEvoy & Stevenson, 2007;Regan, Lee, & Young, 2008;Cambridge Mobile Telematics, 2020). Self-reported surveys or interviews were found useful in collecting information on secondary tasks, especially when comparing the various forms of distraction (Royal, 2003;Lansdown, 2009;McEvoy, Stevenson, & Woodward, 2006;Sullman & Baas, 2004;Young & Lenné , 2010). Surveyed drivers from such studies reported their cellphone use while driving, with the frequency of their use ranging from 13% to 43%. ...
Introduction:
Distracted driving is a concern for traffic safety in the 21st century, and can be held responsible for the increasing propensity and severity of traffic crashes. With the advent of mobile technologies, distractions involving the use of cellphones while driving have emerged, and young drivers in particular are getting more and more engaged in these distractions. Texting or receiving phone calls while driving are offenses in most states, and they are punished with fiscal penalties. Awareness campaigns have also been arranged over recent decades across the United States in order to minimize crashes due to distracted driving. The severity of such crashes depends on driver behavior, which can also be affected by various factors like the geometric design of the roadway, lighting and environmental conditions, and temporal variables.
Method:
In this study, we analyzed data on five years (2015-2019) of crashes involving cellphone use in New Jersey using a mixed logit model. As estimated model parameters can vary randomly across roadway segments in this approach, this allowed us to account for unobserved heterogeneities relating to roadway characteristics, environmental factors, and driver behavior. A pseudo-elasticity analysis was further employed to observe the sensitivity of the significant explanatory variables to crash severity.
Results:
We found that higher speed limits and a larger total number of vehicles involved both increased crash severity, while higher annual average daily traffic (AADT) levels and the presence of an urban road setting reduced it.
Practical applications:
These findings will help decision-makers to comprehend what the significant contributing factors associated with crash injury severity due to distracted driving are, and how to implement necessary interventions to reduce this severity.
... All the test data were encrypted and stored in a hard drive disk, which were uploaded to the VTTI data service after completing the test. An audio system was chosen for conveying the information in the cases of scenario 2 and 3 because previous research [31,32] proved that visual displays can be highly distracting for the driver. In order to ensure that the proposed system can be used for real-time applications, the B-GLOSA system computes the optimum speed profile at 10 Hz, which means the optimum speed is recalculated every 0.1 s. ...
In this study, a Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA) was developed. The proposed B-GLOSA system was implemented on diesel buses, and field tested to validate and quantify the potential real-world benefits. The developed system includes a simple and easy-to-calibrate fuel consumption model that computes instantaneous diesel bus fuel consumption rates. The bus fuel consumption model, a vehicle dynamics model, the traffic signal timings, and the relationship between vehicle speed and distance to the intersection are used to construct an optimization problem. A moving-horizon dynamic programming problem solved using the A-star algorithm is used to compute the energy-optimized vehicle trajectory through signalized intersections. The Virginia Smart Road test facility was used to conduct the field test on 30 participants. Each participant drove three scenarios, including a base case uninformed drive, an informed drive with signal timing information communicated to the driver, and an informed drive with the recommended speed computed by the B-GLOSA system. The field test investigated the performance of using the developed B-GLOSA system considering different impact factors, including road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated that the proposed B-GLOSA system can produce smoother bus trajectories through signalized intersections, thus producing fuel consumption and travel time savings. Specifically, compared to the uninformed drive, the B-GLOSA system produces fuel and travel time savings of 22.1% and 6.1%, on average, respectively.
... For example, drivers were found to decrease their speed by 18 km/h when using their smartphones while driving (Choudhary and Velaga, 2017). Young and Lenné (2010) found that such reduction in speed is highly common, and they observed it among 78.2% of drivers in their study. ...
Driving while distracted by smartphones is an unsafe behavior and constitutes a serious worldwide road safety issue. In line with the risk homeostasis theory, during high-speed driving, drivers perceive smartphone usage as an unwarranted risk and in most cases refrain from doing so. During low-speed driving, however, drivers often use their smartphones, as they do not perceive this as inherently unsafe, even though it is. The goal of this study was to examine an intervention, based on the risk homeostasis theory, aimed at decreasing the use of smartphones while driving at low speeds. Thirty-seven young drivers participated in the research group that aimed to alter drivers’ risk perceptions, decision making, and behavior. The study also included a control group of 33 young drivers. All of the participants’ smartphone usage was monitored using a dedicated application that measured both the number of times drivers touched their smartphone screens while driving and the driving speed each time the screen was touched. The results indicate that drivers in the research group decreased their smartphone usage while driving, unlike the control group drivers who did not alter their behavior. In conclusion, a risk homeostasis-based intervention can decrease dangerous and unsafe driving behavior, even when such behavior is not perceived as significantly dangerous. Furthermore, additional types of risky and unsafe driving behaviors may be decreased using this type of intervention.
... All the test data were encrypted and stored in a hard drive disk, which were uploaded to VTTI data service after completing the test. An audio system was chosen for conveying the information in the cases of scenario 2 and 3 because previous researches [24,25] have proven that visual display can be highly distracting for the driver. In order to ensure that the proposed system can be used for real-time applications, the B-GLOSA system computes the optimum speed profile at 10 Hz, which means the optimum speed is re-calculated every 0.1 seconds. ...
This paper develops a Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA). The proposed B-GLOSA system is implemented on diesel buses, and field tested to validate and quantify the potential real-world benefits. The developed system includes a simple and easy to calibrate fuel consumption model that computes instantaneous diesel bus fuel consumption rates. The bus fuel consumption model, a vehicle dynamics model, the traffic signal timings, and the re-lationship between vehicle speed and distance to the intersection are used to construct an optimi-zation problem. A moving-horizon dynamic programming problem solved using the A-star algo-rithm is used to compute the energy-optimized vehicle trajectory through signalized intersections. The Virginia Smart Road test facility was used to conduct the field test on 30 participants. Each participant drove three scenarios including a base case uninformed drive, an informed drive with signal timing information communicated to the driver, and an informed drive with the recom-mended speed computed by the B-GLOSA system. The field test investigated the performance of using the developed B-GLOSA system considering different impact factors, including road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated that the proposed B-GLOSA system can produce smoother bus trajectories through signalized in-tersections producing fuel consumption and travel time savings. Specifically, compared to the uninformed drive, the B-GLOSA system produces fuel and travel time savings of 22.1% and 6.1% on average, respectively.
... Epidemiological research documented that the role of driver distraction, as a contributing factor to car crashes, decreases in advanced age (Regev et al., 2017;Wilson and Stimpson, 2010;Young and Lenné, 2010). This decrease has been attributed to self-regulation: older drivers often avoid times and places where distractions abound, such as rush hour traffic (Gliklich et al., 2016;Guo et al., 2017), and they suppress engagement in distracting activities because of their life-long experience (review in Young et al., 2018). ...
It is well established that car driving performance suffers when the driver concurrently engages in a distracting activity, such as talking on a cell phone. The present study investigates whether the effects of driver distraction are short-lived, or rather persist for some time. Age-related differences are evaluated as well. Sixty-three young and 61 older adults were tested in a driving simulator. They were asked to follow a lead car that drove at a constant speed, and to concurrently engage in a pseudorandom sequence of distracting tasks (typing, reasoning, memorizing). When the lead car braked, participants had to brake as well to prevent a collision. The stimulus onset asynchrony between the braking task and the last preceding distraction was 11.49 ± 1.99 s. Each person was tested once in a multitasking condition (as described above), and once in a control condition without distracting tasks. Outcome measures quantified distance keeping and lane keeping while participants braked to the lead car. We found that braking responses differed significantly between conditions; this difference could be interpreted as a combination of performance deficits and compensatory strategies in the multitasking condition compared to the control condition. We also found significant differences between age groups, which could be interpreted similarly. Differences between age groups were less pronounced in the multitasking than in the control condition. All observed effects were associated with participants’ executive functioning. Our findings confirm that distractions have an impact on braking responses, and they document for the first time that this impact can persist for about 11.5 s. We attribute this persistence to a task set effect, and discuss the practical relevance of our findings.
... Several researchers have now undertaken reviews of the driver distraction literature (Basacik & Stevens, 2008;Kircher, 2007;Ranney, 2008;Regan et al., 2008;Stutts et al., 2001;Wallis, 2003;Young et al., 2003). Further, numerous surveys have now been undertaken to provide insight into associated behaviours, attitudes and opinions (Canadian Underwriter, 2016;Horrey et al., 2008;Lansdown, 2012;McEvoy et al., 2006;Privilege Insurance, 2006;RAC Motor Insurance, 2009;Royal, 2003;Schroeder et al., 2013;Speirs et al., 2008;StateFarm, 2017;Young & Lenné, 2010). It is clear that, within pragmatically interpretable variability, we are developing a consensus regarding what drivers are doing, how frequently they are doing it, and how distracting they believe the behaviours to be. ...
This paper reports a survey of engagement with, and ratings of, driver distraction, for undergraduate student drivers. Survey data was collected using an anonymous online questionnaire. 530 respondents contributed to the survey during a seven-year data collection period.
Results indicate that the three internal-to-vehicle behaviours rated as most distracting when driving were ‘writing text messages’, ‘internet use’, and ‘reading text messages’. The three most frequently undertaken distractions were, ‘(interactions with) adults’, ‘daydreaming’, and ‘eating, drinking or smoking’. Considering external-to-vehicle distractions, the top three rated were ‘environmental conditions’, ‘unexpected objects or events’, and ‘animals behaving unexpectedly’; while the most frequently experienced external distractions were ‘people (behaving normally), ‘busy roads’ and ‘official signage’. Some evidence was found that internal-to-vehicle distractions were relatively more distracting than external-to-vehicle ones, along with limited findings showing significant variation in the amount of engagement with distractions over time. Significant predictive models for engagement with distraction were calculated (for both work-related and non-work-related driving) and found to be broadly in agreement with previous research, although accounting for less variance in the models. Significantly greater engagement with distractions was found during non-work-related driving, when compared to work-related.
The data present a picture of ongoing and substantial engagement with distracting behaviours for this population over the data collection period. For example, on a daily or weekly basis, more than three-fifths of respondents reported willingness to read text messages with the vehicle in motion; while just under half indicated that they typically write text messages in the same circumstances. However, the findings do offer some promise that interventions targeted towards non-work-related driving behaviours may be effective to reduce volitional engagement with distractions.
... Lowering motor vehicle speed for mobile phone use while driving is a common task management strategy reported in various studies. [11,16] The current study found that around 20% of participants from engineering college and 12% of participants from medical collage were unlikely or very unlikely to lower their speed if they were using mobile phone while driving. Similarly, a substantial proportion of the study participants reported of not increasing control over the steering while using mobile phone during driving. ...
Background:
Globally, motor vehicle accidents (MVAs) cause around 1.35 million deaths annually. Distracted driving, a risk factor for MVA, includes diversion of attention from driving because of use of mobile phone.
Objectives:
The aim of this study was to determine prevalence of mobile phone use and to explore task management strategies, risk perception and attitude towards mobile phone use while driving among Qassim University students.
Methods:
An online cross-sectional survey among 212 randomly selected medical (n = 83) and engineering students (n = 129) of Qassim University, Saudi Arabia, through semi-structured, self-administered questionnaire, designed using Google forms. The survey was conducted from February to March 2020.
Results:
The overall prevalence of mobile phone use while driving was 93.4% (medical students: 96.4%; engineering students: 91.5%). Around 49.5% participants 'often' or 'always' used mobile phone while driving. Among task management strategies, 169 (79.7%) participants were 'likely' or 'very likely' to lower their driving speed while 90 (42.5%) were 'likely' or 'very likely' to increase control over the steering while using mobile phone during driving. Regarding risk perception, 173 (81.6%) participants thought that they were 'unlikely' or 'very unlikely' to have MVA on looking at phone continuously for more than 2 s, and 185 (87.3%) participants thought that they were 'unlikely' or 'very unlikely' to have MVA by texting or browsing while driving. Thirty-six (17%) participants reported MVA because of distraction by mobile phone use while driving.
Conclusion:
High prevalence of mobile phone use during driving and low perceived risk of experiencing MVA because of mobile phone use was found among Qassim University students. Creating awareness on risks of mobile phone use while driving is recommended.
... We hypothesized that participants with visual quality degradation would show greater impairment of their driving behavior when both visual and cognitive demands are the highest (i.e., during the navigation task in the highway scenario) and that this effect would be more important with a higher visual quality degradation. Both speed [37][38][39][40][41][42][43] and SDLP [44][45][46][47] are widely studied variables in order to understand people's reactions to driving. We propose the hypothesis that the speed will be modulated by an increase in its variability as well as a decrease in its average and that SDLP will undergo an increase. ...
Having an optimal quality of vision as well as adequate cognitive capacities is known to be essential for driving safety. However, the interaction between vision and cognitive mechanisms while driving remains unclear. We hypothesized that, in a context of high cognitive load, reduced visual acuity would have a negative impact on driving behavior, even when the acuity corresponds to the legal threshold for obtaining a driving license in Canada, and that the impact observed on driving performance would be greater with the increase in the threshold of degradation of visual acuity. In order to investigate this relationship, we examined driving behavior in a driving simulator under optimal and reduced vision conditions through two scenarios involving different levels of cognitive demand. These were: 1. a simple rural driving scenario with some pre-programmed events and 2. a highway driving scenario accompanied by a concurrent task involving the use of a navigation device. Two groups of visual quality degradation (lower/ higher) were evaluated according to their driving behavior. The results support the hypothesis: A dual task effect was indeed observed provoking less stable driving behavior, but in addition to this, by statistically controlling the impact of cognitive load, the effect of visual load emerged in this dual task context. These results support the idea that visual quality degradation impacts driving behavior when combined with a high mental workload driving environment while specifying that this impact is not present in the context of low cognitive load driving condition.
Mobile phone use is one of the most frequent causes of distraction among drivers. While there have been a significant number of studies that have examined individuals' intentions to use a mobile phone while driving, the influence of individuals' in-situ judgement of driving conditions has received considerably less attention. The aim of this investigation was to provide a systematic understanding of how factors associated with the driving context and environment influence a driver's decision to engage in mobile phone use while driving. Following a systematic classification scheme, 41 research articles from the years 2011 to 2020 were reviewed and synthesised to identify the contextual determinants of mobile phone distraction. Overall, the findings provided support for the role that contextual features play in influencing individuals' mobile phone use engagement. This finding was particularly the case in instances where mobile phone tasks required relatively high cognitive and physical demands on an individual, such as texting and/or reading mails. The findings also indicated that as contextual complexity increases, mobile phone use decreases as well. A deeper understanding of the relationship between contextual factors and phone use while driving may aid in the design of more efficient driver support systems and the development of distraction-sensitive road design guides. This understanding can also assist in the identification of mobile phone use hotspots and the improvement of law enforcement and educational strategies to prevent the behaviour.
While the number of electric vehicle traffic accidents has increased consistently over recent years, this has resulted in significant property losses and personal risks for drivers and passengers. To alleviate the above issues, this paper proposes a novel hybrid gated recurrent unit and temporal convolutional (GRUTC) neural network to evaluate driving safety. First, an evaluation framework consisting of ten indicators is constructed, including evaluating component safety and risky driving behaviors. Second, a long-short sample scoring rule is proposed to obtain a comprehensive evaluation of driving safety. The safety score is first evaluated based on the short samples, consisting of eight actual sampling points. Then, a comprehensive safety score is evaluated based on a long sample, which consists of nine consecutive short samples. Third, all four datasets are evaluated to establish the evaluation model. Verified by the yearlong operation data of dataset #1, the proposed method shows higher evaluating performance than commonly used methods. More importantly, a novel transfer learning method based on accumulated training with progressive datasets is proposed to improve the generalization. A stable and remarkable evaluating accuracy is obtained with the MAPE of 2.68% when the model is directly tested with dataset #4 after two transfers. This paper aims to assess the driving safety of real vehicles employing “driver-vehicle-road” multidimensional indicators and to improve the accuracy of the assessment by using the GRUTCN model.
Driving safety is a concern primarily in young and older drivers. Even though most traffic accidents happen due to human factors, studies do not consider visual behavior variables to model driving safety. In this study, we used the Intelligent Multimodal Monitoring System to Infer Points of Visual Attention (SiMIPAV) to collect data from 10 young drivers to analyze the feasibility of using visual behaviors in assessing driving safety. To this end, participants’ driving performance was evaluated using on-road driving tests and cognitive measures. We see the potential to generate models that predict cognitive maturity to drive from visual behavior properties. Specifically, the frequency of on-road visual behavior could be used to determine scanning skills.
Driver assistance systems are designed to increase comfort and safety by automating parts of the driving task. At the same time, modern in-vehicle information systems with large touchscreens provide the driver with numerous options for entertainment, information, or communication, and are a potential source of distraction. However, little is known about how driving automation affects how drivers interact with the center stack touchscreen, i.e., how drivers self-regulate their behavior in response to different levels of driving automation. To investigate this, we apply multilevel models to a real-world driving dataset consisting of 31,378 sequences. Our results show significant differences in drivers’ interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. Specifically, at higher levels of driving automation (level 2), the mean glance duration toward the center stack touchscreen increases by 36% and the mean number of interactions per sequence increases by 17% compared to manual driving. Furthermore, partially automated driving has a strong impact on the use of more complex UI elements (e.g., maps) and touch gestures (e.g., multitouch). We also show that the effect of driving automation on drivers’ self-regulation is greater than that of vehicle speed and road curvature. The derived knowledge can inform the design and evaluation of touch-based infotainment systems and the development of context-aware driver monitoring systems.
Distracted driving is a prominent cause of traffic crashes and may increase the severity of collisions. Due to the larger speeds on toll ways, distracted driving crashes are more severe than on other types of roads, making it worthwhile to investigate. This study examined the variation in the influence of factors affecting injury severity in crashes involving distracted and non-distracted driving, as well as the change over time, using crash data from Florida toll ways from the 2017 to 2019. A series of random parameters logit models with heterogeneity in the means and variances were developed to analyze different driver-injury severities (no injury, minor injury, and severe injury) in crashes involving distracted and non-distracted driving. In addition, likelihood ratio tests were conducted to determine whether model parameters differed between different driver behaviors (distracted/non-distracted driving) and among years. Several factors potentially impacting injury severities were studied, including driver, crash, vehicle, roadway, environment, temporal, and others. Significant disparities were observed between the contributing factors of the severity of crashes involving distracted and non-distracted driving. Results showed that considerable differences were also observed in the severity of injuries caused by two types of crashes and distracted driving resulted in more serious crashes than non-distracted driving. Despite model results indicated that factors influencing injury severity altered over time, several factors, such as motorcycle involvement and commercial car involvement, still exhibited relative temporal stability in non-distracted driving crashes over the three years. Temporal instability and non-transferability were also captured by out-of-sample predictions to verify the temporal shifts of contributing variables from year to year. This study is useful for distinguishing the influence mechanisms between the two types of crashes involving distracted and non-distracted driving, and the results can be applied for safety countermeasures development.
Young drivers are more likely to be involved in traffic accidents. The study aims to explore mechanisms behind distracted driving behaviour, traffic safety environment, driving responsibility, and hazard perception. A conceptual model is proposed based on Stimulus-Organism-Response (S-O-R) theory. The self-reported data from 367 drivers are used to estimate and modify the model based on exploratory factor analysis, structural equation modelling, and bias-corrected bootstrap method. The regression relationships and the mediators have been identified. The traffic safety environment including the traffic enforcement and the driving condition isn’t related to the distracted driving behaviour. The traffic enforcement is associated the driving responsibility, the relationships between the driving responsibility, the hazard perception and the driving condition are significant, and the relationships between the distracted driving behaviour, the driving responsibility and the hazard perception are noteworthy. A positive traffic safety environment is beneficial to the safety of young drivers. The sense of driving responsibility and the self-cognition of hazard perception need attention for the early intervention of young drivers’ distracted driving behaviours.
Background
Cell phone use while driving is a significant safety problem all around the world. It is considered one of the main factors contributing to road crashes among young drivers.
Aim
To address this problem, it is important to determine how young drivers perceive the risk of using a cell phone while driving and to understand whether the perception of risk is correlated with their crash involvement.
Methods
Data were collected through a detailed questionnaire from young drivers in Qatar to assess potential correlations between the drivers’ demographic background, perception of risk, and crash involvement. Logistic regression models were developed to explore the relationships between those variables.
Results
The analysis revealed that female drivers had a higher perception of risk related to using cell phones while driving compared to male drivers. Drivers with higher education levels were found to also have a higher perception of risk when compared to less educated drivers. The analysis showed that participants who perceived lower risk of answering a call while driving were more likely to be involved in a crash.
Conclusion
These results can be useful to identify the groups that should be targeted through countermeasures. Different countermeasures were presented, and directions for future research were proposed.
Research on distracted driving due to phone use has increased substantially over the past decades, however, very little is explored about commercial vehicle drivers (e.g., truck drivers) in this aspect. This study focused on examining the prevalence of phone use habits and the associated crash risk for data collected from 490 Indian truck drivers. The data on demographic details, driving history, phone use habits (in everyday life and during driving), history of receiving any penalty for phone use and incidences of crash occurrence, was collected through face-to-face interviews with the drivers. Binary logistic models were used to identify the factors affecting phone use habits during driving and the associated crash risk. Further, the incidences of receiving a penalty for the phone use were examined through cross-tabulation and chi-square statistics. The results showed that 55% of the drivers used a phone during driving, mainly for talking purpose. The model revealed that education, vehicle size, vehicle ownership and everyday life phone use habits were the significant factors associated with phone use while driving. Regarding the history of penalty receiving incidences, 41% of the drivers who used a phone during driving had received the penalty, and 52% of these penalty-receiving drivers were penalized repetitively. The model results for crash risk showed that the frequent phone users were 29 times more likely to be involved in a crash due to phone use compared to the non-frequent users. The results suggest a double level (legislative and company level) prohibition policy for phone use during driving for the truck drivers and also to enforce strict and effective legislative ban especially on the truck routes.
Transport has a great impact on human activities but contributes to many negative phenomena occurring in road traffic, for example, road traffic accidents, emission of toxic exhaust fumes into the atmosphere and a high share of cars in road traffic. For the above reasons, many initiatives have been taken in the field of road traffic management and urban logistics. Based on a literature review, it was found that the problem of the phenomenon of traffic congestion in urban areas remains an ongoing issue. In the first part of this article, the theoretical issues of traffic flow and congestion formation in the city road networks were presented. While the second part outlines the situation of transport congestion in 10 Polish cities based on the worldwide TomTom Traffic Index in the years 2008-2018. This study is a brief analysis of the trends relating to transport congestion based on the TomTom Traffic Index in these cities.
This research implemented both qualitative and quantitative methods to 1) explore young drivers’ (aged between 17 and 25 years) awareness and perceptions of legal sanctions associated with phone use while driving and 2) identify whether the accuracy of their knowledge influences deterrence-related perceptions. In the qualitative phase, 60 Queensland motorists participated in focus groups. The findings of the focus groups highlighted that greater awareness of the penalty for phone use while driving would enable this punishment to act as a more salient deterrent. More specifically, the penalty for hands-free phone use was considered too high, whereas when the penalty was applied to hand-held phone use it was considered reasonable, with some commenting that increasing the fine could be a greater deterrent. However, the penalty also appeared to be linked to the perceived legitimacy of the rule. The quantitative phase utilised a cross-sectioanl survey design and consisted of 503 drivers. Overall, more participants appear to be underestimating (63% underestimated the fine and 37% underestimated the demerit points) as opposed to overestimating (14% overestimated the fine and 22% overestimated the demerit points) the penalty for phone use while driving. As expected, compared to those who accurately estimated the extent of the punishment (both the monetary sanction and the number of demerit points) associated with phone use while driving, drivers who underestimated the phone punishment (points and fine) had significantly lower perceptions of the severity of punishment. These findings suggest that some young drivers do not have sufficient knowledge of mobile phone sanctions, which has significant implications for ongoing attempts to maximise deterrent mechanisms.
Understanding how changes in visual and attentional behaviors impact driving as we age is still a subject studied by the research community. However, little attention has been paid to using sensing and AI techniques to conduct such studies. We present a multi-sourced intelligent sensing system that infers the visual point of attention (VPoA) associated with five vehicle’s cockpit zones with an accuracy of 98%. The VPoA is inferred from the pitch, yaw, and roll angles of head movements captured with inertial sensors and a facial recognition application. The system also includes a tablet-based application that automatically collects data from the driving context, e.g., speed and location. It also enables an annotator to add observed drivers’ actions, e.g., interactions with a passenger. We conducted a naturalistic study with 15 younger adults and 15 older adults to demonstrate the system’s efficacy to identify visual behavior patterns similar to those identified in previous studies that have used traditional data collection methods. A new finding is that the younger group looks more frequently at the lap than the elderly group, independently if a passenger was present. The
Lap
was the VPoA associated with using the cellphone.
Road traffic collisions are the leading cause of death for those between the ages of 15–29, according to the World Health Organisation. This study investigates one of the primary reasons for the high fatality rate amongst Young Novice Drivers (YNDs) – their use of smartphones while driving. We gathered responses from a representative sample of YNDs on their behaviour while driving using an updated version of the ‘Behaviour of Young Novice Drivers Scale’. Survey responses totalled 700 YNDs situated throughout Germany. From these responses, we examined the prevalence of certain driving behaviours that are described as ‘distracting’ and compared these driving behaviours to the respondents’ use of specific smartphone features. The responses report that music-related activities (e.g. changing music on a smartphone) are most common amongst YNDs. Speaking on the phone is seldom-reported, although more males than females indicated engagement in this behaviour. We further carried out a correlation analysis and correspondence analysis. On that basis we found that those who report speaking on a smartphone are significantly more likely to engage in driving behaviours with potentially fatal consequences, such as speeding and driving while impaired by prohibited substances (drugs, alcohol). We propose that the results could be used by policymakers for public information implications and to tailor financial penalties for those engaging in smartphone behaviours that are linked to harmful driving behaviours. In addition, our findings can also be used in a Usage-based Insurance (UBI) context to financially incentivise safer driving.
This study analyzed factors affecting behavior of mobile phone use while driving and its effects on driving performance, in terms of speed, lateral position, steer deviation, steer speed, following distance, perception–reaction time, and occurrence of a near miss situation. To investigate the factors affecting behavior of mobile phone use while driving, 1106 respondents from four different regions in Thailand participated in the questionnaire survey study. Theory of Planned Behavior (TPB) was used to explain these factors including two additional extended factors which are risk perception and law enforcement knowledge. The outcome of this part shows that attitude, norm, and law enforcement knowledge significantly affect the intention and behavior of the drivers. Even though approximately 90 percent of drivers realized that using a mobile phone while driving was dangerous and against the law, they have reported that they still use mobile phone while driving. To determine the effect of mobile phone use on driving performance, a 2-lane, straight rural highway, with a leading vehicle and an unexpected “STOP” sign, were simulated in order to examine the driving performance of drivers “without a phone”, “talking on a phone call”, and “texting a message” conditions. The results found that using mobile phone while driving can reduce speed and following distance, but increase lateral deviation, steer deviation, steer speed, perception-reaction time, and number of near misses leading to higher risks for road crashes.
This paper reviews two road-user surveys on the use of mobile phones on the road in Finland where the mobile phone ownership rate is highest in the world (70% in August 2000). From 1998 to 1999 the proportion of drivers that chose to use a mobile phone while driving rose from 56% to 68%, while the proportion of phone using drivers who experienced dangerous situations due to phone use rose from 44% to 50%. The proportion of drivers who used their phones in some way to benefit safety on the road remained at about 55%. The youngest, novice drivers had the highest level of phone usage of all age categories. Over 48% of the interviewees believed that the government should ban the use of hand-held mobile phones while driving, and another 27% believed that all types of mobile phone use should be banned while driving. Those drivers who used their phones the most each day were more likely to want some form of restrictions, than those who had lower usage. This is a strong message to the elected lawmakers and raises the problem of exactly how regulatory bodies would go about controlling the future growth of new driver support and non-driving related communication devices in road vehicles. It was concluded that legislating for hands-free use only would be a reasonable course of action. Mandating that the current generation of equipment should be optimized for hands-free use should result in future generations of in-vehicle equipment also being optimized for hands-free use as a minimum criterion.
Five years (1995-1999) of national Crashworthiness Data System (CDS) data are analyzed to determine the role of driver distraction in traffic crashes and the specific sources of this distraction. Results show that 8.3 percent of the drivers were distracted at the time of their crash; after adjustment for the large percentage of drivers with unknown distraction status, the percentage rose to 12.9 percent. The most frequently cited sources of driver distraction were persons, objects or events outside the vehicle (29.4% of distracted drivers), adjusting the radio, tape or CD player (11.4%), and other occupants in the vehicle (10.9%). Other specific distractions (moving objects in vehicle, other objects brought into vehicle, adjusting vehicle or climate controls, eating and drinking, cell phones, and smoking) were each cited in only one to four percent of the cases. The likelihood of being distracted and the source of distraction varied by driver age but not by gender. Results are discussed in light of the limitations inherent in the CDS and other crash data, and the need for expanded data collection initiatives.
The prevalence of automobile drivers talking on cell phones is growing, but the effect of that behavior on driving performance is unclear. Also unclear is the relationship between the difficulty level of a phone conversation and the resulting distraction.
This study used a driving simulator to determine the effect that easy and difficult cell phone conversations have on driving performance.
Cell phone use caused participants to have higher variation in accelerator pedal position, drive more slowly with more variation in speed, and report a higher level of workload regardless of conversation difficulty level.
Drivers may cope with the additional stress of phone conversations by enduring higher workloads or setting reduced performance goals.
Because an increasing number of people talk on the phone while driving, crashes caused by distracted drivers using cell phones will cause disruptions in business, as well as injury, disability, and permanent loss of personnel.
Our research examined the effects of hands-free cell phone conversations on simulated driving. We found that driving performance of both younger and older adults was influenced by cell phone conversations. Compared with single-task (i.e., driving-only) conditions, when drivers used cell phones their reactions were 18% slower, their following distance was 12% greater, and they took 17% longer to recover the speed that was lost following braking. There was also a twofold increase in the number of rear-end collisions when drivers were conversing on a cell phone. These cell-phone-induced effects were equivalent for younger and older adults, suggesting that older adults do not suffer a significantly greater penalty for talking on a cell phone while driving than compared with their younger counterparts. Interestingly, the net effect of having younger drivers converse on a cell phone was to make their average reactions equivalent to those of older drivers who were not using a cell phone. Actual or potential applications of this research include providing guidance for recommendations and regulations concerning the use of mobile technology while driving.
Unobtrusive video camera units were installed in the vehicles of 70 volunteer drivers over 1-week time periods to study drivers' exposure to distractions. The video data were coded based on a detailed taxonomy of driver distractions along with important contextual variables and driving performance measures. Results show distractions to be a common component of everyday driving. In terms of overall event durations, the most common distractions were eating and drinking (including preparations to eat or drink), distractions inside the vehicle (reaching or looking for an object, manipulating vehicle controls, etc.), and distractions outside the vehicle (often unidentified). Distractions were frequently associated with decreased driving performance, as measured by higher levels of no hands on the steering wheel, eyes directed inside rather than outside the vehicle, and lane wanderings or encroachments. Naturalistic driving studies can provide a useful supplement to more controlled laboratory and field studies to further our understanding of the effects of all types of distractions on driving safety.
To quantify the prevalence and effects of distracting activities while driving.
Cross sectional driver survey.
New South Wales and Western Australia, Australia.
1347 licensed drivers aged between 18 and 65 years. Data were weighted to reflect the corresponding driving population.
Prevalence of distracting activities while driving; perceived risks and adverse outcomes due to distractions.
The most common distracting activities during the most recent driving trip were lack of concentration (weighted percentage (standard error, SE) 71.8% (1.4%) of drivers); adjusting in-vehicle equipment (68.7% (1.5%)); outside people, objects or events (57.8% (1.6%)); and talking to passengers (39.8% (1.6%)). On average, a driver engaged in a distracting activity once every six minutes. One in five crashes (21%) during the last three years, involving one in 20 drivers (5.0% (0.7%)), was attributed to driver distraction based on self-report. In the population under study, this equated to 242,188 (SE 34,417) drivers. Younger drivers (18-30 years) were significantly more likely to report distracting activities, to perceive distracting activities as less dangerous, and to have crashed as a result.
Distracting activities while driving are common and can result in driving errors. Driver distraction is an important cause of crashes. Further research is needed to estimate the risk conferred by different distracting activities and the circumstances during which activities pose greatest risk. These results suggest that a strategy to minimize distracting activities while driving, with a focus on young drivers, is indicated.
To explore the use and effects of using mobile phones while driving.
Cross-sectional survey.
New South Wales and Western Australia, 20 October to 7 November 2003.
1347 licensed drivers aged 18 to 65 years. Data were weighted to reflect the corresponding driving population in each state.
Mobile phone use while driving (hand-held, hands-free and text messaging); adverse effects of use.
While driving, an estimated 57.3% +/- 1.5% of drivers have ever used a mobile phone and 12.4% +/- 1.0% have written text messages. Men, younger drivers and metropolitan residents were more likely to use a phone while driving and to report a higher frequency of use. Enforcement of hand-held phone restrictions was perceived to be low (69.0% +/- 1.5%) and an estimated 39.4% +/- 2.1% of people who phone while driving use a hand-held phone. Half of all drivers (50.1% +/- 1.6%) did not agree with extending the ban to include hands-free phones. Among drivers aged 18-65 years in NSW and WA, an estimated 45 800 +/- 16 466 (0.9% +/- 0.3%) have ever had a crash while using a mobile phone and, in the past year, 146 762 +/- 26 856 (3.0% +/- 0.6%) have had to take evasive action to avoid a crash because of their phone use.
Phone use while driving is prevalent and can result in adverse consequences, including crashes. Despite legislation, a significant proportion of drivers continue to use hand-held mobile phones while driving. Enhanced enforcement is needed.
To evaluate change in handheld mobile telephone (mobile) use among motor vehicle drivers between 2002 and 2006.
Observational study of motor vehicle drivers at three times (10:00-11:00; 14:00-15:00; 17:00-18:00) on three consecutive Tuesdays in October 2006 at 12 highway sites in metropolitan Melbourne.
Rates of handheld mobile use overall and by the sex and age of drivers, highway site (major metropolitan road, central business district, freeway exit ramp) and time of day.
In 2002, 315 of 17 023, and in 2006, 331 of 20 207 drivers were observed using handheld mobiles. This represented a non-significant rate decrease from 18.5 to 16.3 users/1000 drivers (rate difference, 2.1 users/1000 drivers; 95% CI,- 0.6 to 4.8; P = 0.07). Unlike 2002, the rate of handheld mobile use among men in 2006 was significantly higher than for women (rate difference, 3.7 mobiles/1000 drivers; 95% CI, 0.1-7.3; P = 0.03). In both 2002 and 2006, mobile use was most common in the central business district. In 2002, there was significantly more mobile use in the evening, while in 2006, the evening rate was significantly lower than the morning rate (rate difference, 4.3; 95% CI, - 0.1 to 8.7; P = 0.03) and slightly lower than the afternoon rate (rate difference, 3.0; 95% CI, - 1.1 to 7.1; P = 0.08). The effect of age remained unchanged between 2002 and 2006, with older drivers using mobiles least (P < 0.001).
The number of drivers at risk from handheld mobile phone use remains almost unchanged. However, a slight reduction in the rate of use overall and variations in use among driver subgroups are apparent. Policing and public awareness campaigns need to further address this preventable risk of injury.
This paper reports a simulator-based study of the effects of mobile phone use on driving performance. Changes in heart rate indicated that mobile phone use increases the cognitive demand experienced by drivers with, it is argued, consequent reduction in safety margins. However, experimental results also suggested that participants engaged in a process of risk compensation, with driving speed being slower at times of mobile phone conversation while the number of off-road excursions (OFFS) and collisions remained stable. There also was some evidence that the use of a hand-held mobile phone (when compared to a hands-free system) was associated with poorer driving performance. Implications for `real world' driving are considered.
In four field experiments the participants drove an instrumented car provided with a hands-free phone and performed several cognitive tasks while driving including phone conversations. The study focussed the cognitive component of the conversations, excluding dialling. The cognitive demands of the conversations were varied and in two of the experiments the same tasks had two versions: by phone and in live conversation with the experimenter in the car. Several dependent measures like visual search behaviour, driving speed, visual detection and response selection capacities and others were analysed. Like in previous experiments of the same authors the more demanding cognitive tasks produced higher interference effects, but when the same tasks performed by phone were compared with its live versions no differences were observed. Once the manual phone operation has been technically suppressed the risk of phone conversations relies on the demands of the message content and its equivalent to talking to a passenger. Implications for safety are discussed.
Driving performance in an instrumented vehicle was compared with performance in a low-cost, fixed-based driving simulator. Six men and six women drove a freeway route while periodically dialling simulated phone calls. The same subjects drove a laboratory driving simulator using two visual fidelity levels: a colour scene with relatively high detail, and a monochrome (night) scene showing only road-edge markings. Lane position, speed, steering-wheel angle and throttle position were recorded in both contexts. Lane-keeping in the simulator was less precise than on the road, but speed control performance was comparable. The SD of lane position in normal driving was about twice as large, on average, in the simulator (0.360 versus 0.165 m). Lane keeping and speed control were less precise when dialling the phone than in normal driving, both in the simulator and on the road, but the performance decrement was greater in the simulator. The addition of the phone task increased the mean lateral speed in the car by about 43%, while in the simulator the mean lateral speed increased by 158% with the addition of the phone task. Subjects >60 years of age showed larger performance decrements during a concurrent phone dialling task than did subjects 20–30 years of age both in the simulator and on-road. No important differences in driving performance were found between the high and low simulator scene fidelity levels. The simulator demonstrated good absolute validity for measures of speed control and good relative validity for the effects of the phone task and age on driving precision.
Word Count: 5,839 words + 7 tables and figures = 7,589 words TRB 2003 Annual Meeting CD-ROM Paper revised from original submittal.
This study examined the impact of cell phone conversation on situation awareness and performance of novice and experienced drivers. Driving performance and situation awareness among novice drivers ages 14–16 (n = 25) and experienced drivers ages 21–52 (n = 26) were assessed using a driving simulator. Performance was measured by the number of driving infractions committed: speeding, collisions, pedestrians struck, stop signs missed, and centerline and road edge crossings. Situation awareness was assessed through a query method and through participants’ performance on a direction-following task. Cognitive distractions were induced through simulated hands-free cell phone conversations. The results indicated that novice drivers committed more driving infractions and were less situationally aware than their experienced counterparts. However, the two groups suffered similar decrements in performance during the cell phone condition. This study provides evidence of the detrimental effects of cell phone use for both novice and experienced drivers. These findings have implications for supporting driving legislation that limits the use of cell phones (including hands-free) in motor vehicles, regardless of the driver’s experience level.
As part of the HASTE European Project, effects of visual and cognitive demand on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). The present paper reports results from simulated and real motorway driving. Data were collected in a fixed base simulator, a moving base simulator and an instrumented vehicle driven in real traffic. The data collected included speed, lane keeping performance, steering wheel movements, eye movements, physiological signals and self-reported driving performance. The results show that the effects of visual and cognitive load affect driving performance in qualitatively different ways. Visual demand led to reduced speed and increased lane keeping variation. By contrast, cognitive load did not affect speed and resulted in reduced lane keeping variation. Moreover, the cognitive load resulted in increased gaze concentration towards the road centre. Both S-IVIS had an effect on physiological signals and the drivers’ assessment of their own driving performance. The study also investigated differences between the three experimental settings (static simulator, moving base simulator and field). The results are discussed with respect to the development of a generic safety test regime for In-vehicle Information Systems.
Research has shown that using a mobile phone whilst driving may increase the risk of being crash involved by as much as nine times. As around 65% of New Zealand's population own mobile phones, this represents a potentially very significant hazard. In order to effectively target interventions towards those drivers who use mobile phones while driving, information is needed about the characteristics of these drivers. The present study investigated the frequency of mobile phone use on New Zealand's roads and the characteristics of drivers who use mobile phones while driving. The research found that more than half (57.3%) of the participants used a mobile phone at least occasionally while driving. Those who reported using a mobile phone more often whilst driving tended to; be male, reside in a main urban area, report a higher annual mileage, drive a later model car with a larger engine, prefer a higher driving speed, have less driving experience (in years) and to be younger. In line with previous research, there was also a significant relationship between crash involvement and use of a mobile phone whilst driving. However, once the contributions of the demographic and descriptive variables had been partialled out, using hierarchical logistic regression, the relationship between crash involvement and mobile phone use was no longer significant.
Using a hand-held mobile phone whilst driving has been linked to an increased risk of being involved in a road crash. Little research, however, has been done on actual road exposure rates to this potential safety problem. The main aim of this study was therefore to establish the number of drivers who use hand-held mobile phones while driving, and to discover if this number had increased as compared to similar observations obtained one year earlier. 40 roadside observations were made at major roads during daylight hours. On average approximately 1.5% of all vehicle drivers were observed using hand-held mobile phones. No significant difference was found between the percentage of drivers observed to be using phones in an earlier study and this current study. In addition, separate observations were undertaken at four sites at four periods of the 'normal' working day to establish if a possible time of day effect existed. The data found that the level of mobile phone use did not significantly differ during the day, however, as a percentage of vehicle flow, the highest use period was between 11.00 and 12.00. Finally, for the same four sites, supplementary measures were taken to establish personal characteristics of the phone users. It was found that phone users were predominantly male (78%) and less than 40-years old (64%).
As computer applications for cars emerge, a speech-based interface offers an appealing alternative to the visually demanding direct manipulation interface. However, speech-based systems may pose cognitive demands that could undermine driving safety. This study used a car-following task to evaluate how a speech-based e-mail system affects drivers' response to the periodic braking of a lead vehicle. The study included 24 drivers between the ages of 18 and 24 years. A baseline condition with no e-mail system was compared with a simple and a complex e-mail system in both simple and complex driving environments. The results show a 30% (310 ms) increase in reaction time when the speech-based system is used. Subjective workload ratings and probe questions also indicate that speech-based interaction introduces a significant cognitive load, which was highest for the complex e-mail system. These data show that a speech-based interface is not a panacea that eliminates the potential distraction of in-vehicle computers. Actual or potential applications of this research include design of in-vehicle information systems and evaluation of their contributions to driver distraction.
In light of the rapidly increasing development of the cell phone market, the use of such equipment while driving raises the question of whether it is associated with an increased accident risk; and if so, what is its magnitude. This research is an epidemiological study on two large cohorts, namely users and non-users of cell phones, with the objective of verifying whether an association exists between cell phone use and road crashes, separating those with injuries. The Société de l'Assurance Automobile du Québec (SAAQ) mailed a questionnaire and letter of consent to 175000 licence holders for passenger vehicles. The questionnaire asked about exposure to risk, driving habits, opinions about activities likely to be detrimental to driving and accidents within the last 24 months. For cell phone users, questions pertaining to the use of the telephone were added. We received 36078 completed questionnaires, with a signed letter of consent. Four wireless phone companies provided the files on cell phone activity, and the SAAQ the files for 4 years of drivers' records and police reports. The three data sources were merged using an anonymized identification number. The statistical methods include logistic-normal regression models to estimate the strength of the links between the explanatory variables and crashes. The relative risk of all accidents and of accidents with injuries is higher for users of cell phones than for non-users. The relative risks (RR) for injury collisions and also for all collisions is 38% higher for men and women cell phone users. These risks diminish to 1.1 for men and 1.2 for women if other variables, such as the kilometres driven and driving habits are incorporated into the models. Similar results hold for several sub-groups. The most significant finding is a dose-response relationship between the frequency of cell phone use, and crash risks. The adjusted relative risks for heavy users are at least two compared to those making minimal use of cell phones; the latter show similar collision rates as do the non-users.
To determine the rate of handheld mobile telephone use among motor vehicle drivers.
Observational study of motor vehicle drivers at three times (10: 00-11: 00; 14: 00-15: 00; 17: 00-18: 00) on three consecutive Fridays in October 2002 at 12 highway sites in metropolitan Melbourne.
Rates of mobile phone use overall and by sex and age group, highway site (major metropolitan road, central business district, freeway exit ramp) and time of day (morning, afternoon, evening).
315 of 17 023 drivers were observed using mobile phones (18.5 users/1000 drivers; 95% CI, 16.5-20.6). Men had a slightly higher rate of use (19.0; 95% CI, 16.5-21.6) than women (17.5; 95% CI, 14.1-20.9), but the difference was not significant. Older drivers (50 years or more) had a significantly lower rate (4.8; 95% CI, 2.5-7.0) than middle-aged (21.9; 95% CI, 18.8-25.1) or young drivers (23.2; 95% CI, 18.9-27.5). Central business district drivers had a slightly, but not significantly, higher rate (20.5; 95% CI, 16.8-24.3) compared with those on major metropolitan roads (16.7; 95% CI, 13.3-20.2) or freeway exit ramps (18.2; 95% CI, 14.8-21.6). The rate of mobile phone use was significantly higher in the evening (23.5; 95% CI, 19.8-27.3) compared with the morning (16.0; 95% CI, 12.6-19.4) and afternoon (15.2; 95% CI, 11.9-18.4).
Mobile phone use is common among Melbourne metropolitan drivers despite restrictive legislation. This issue needs to be further addressed by Victoria Police and public health and education agencies. Similar research is indicated to determine the extent of mobile phone use in other states.
A number of epidemiological studies have reported drivers who use a mobile phone while driving have an elevated risk of being involved in a crash. This is particularly concerning as a survey of drivers in the Spanish region of Catalunya found that approximately 87% own mobile phones. The present study investigated the reported frequency of mobile phone use on Spanish roads (for talking and using SMS), the characteristics of the drivers who use mobile phones while driving and whether they altered their driving behaviour when using a mobile phone. The research found that more than 60% use a mobile phone while driving and that the phone is mostly used for making calls, rather than using SMS. In general, males and females use mobile phones about the same reported frequency, although males were more likely to use a mobile phone to talk on the highway. The pattern for age was the same for both male and female participants, with the younger drivers using SMS more frequently than older drivers. On urban roads almost half of the drivers reported changing their driving behaviour when using a mobile phone, while on the highway this figure was slightly over 41%. The reported frequency of using a mobile phone to talk on urban roads was significantly correlated with crash involvement. However, this affect disappeared once the contributions of the demographic and descriptive variables had been partialled out.
The study's objectives were to determine the prevalence and types of distracting activities involved in serious crashes, and to explore the factors associated with such crashes. We interviewed 1367 drivers who attended hospital in Perth, Western Australia between April 2002 and July 2004 following a crash. A structured questionnaire was administered to each driver and supplementary data were collected from ambulance and medical records. Over 30% of drivers (433, 31.7%) cited at least one distracting activity at the time of crashing and driver distraction was reported to have contributed to 13.6% of all crashes. The major distracting activities were conversing with passengers (155, 11.3%), lack of concentration (148, 10.8%) and outside factors (121, 8.9%). Using logistic regression, a distracting activity at the time of a crash was significantly more likely among drivers with shorter driving experience (0-9 years, 38.3% versus >or=30 years, 21.0%, p<0.001). Distracting activities at the time of serious crashes are common and can cause crashes, and the types of activities reported are varied. Increased driver awareness of the adverse consequences of distracted driving with a focus on novice drivers, enforcement of existing laws (e.g. those requiring a driver to maintain proper control of a vehicle), and progress on engineering initiatives (such as collision warning systems) are needed to reduce injury.
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