The influence of roadway situation, other contextual factors, and driver characteristics on the prevalence of driver secondary behaviors

Kidd, David G.; Tison, Julie; Chaudhary, Neil K; McCartt, Anne Taylor; Casanova-Powell, Tara

doi:10.1016/j.trf.2016.06.004

Cited by 61 publications

(40 citation statements)

References 20 publications

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“…On average, the likelihood of reporting being likely or very likely to engage in mobile phone multitasking decreases by 0.054 for every unit increase in perceived workload. This finding replicated previous studies that have confirmed lower intentions of mobile phone engagement in the presence of increased traffic demands (Hancox et al., ; Kidd et al., ). Nonetheless, the unobserved heterogeneity detected around this variable could be explained using the minimum required attention (MiRA) theory (Kircher & Ahlstrom, ).…”

Section: Discussionsupporting

confidence: 91%

“…Knowing that drivers are deterred by the presence of police enforcement supports the implementation of high‐visibility enforcement programs to combat distracted driving (Bates, Soole, & Watson, ). This research also shows that drivers prefer to use their phone when the traffic conditions are less demanding, as has been noticed in previous research, e.g., stopped at red lights (Kidd, Tison, Chaudhary, McCartt, & Casanova‐Powell, ) or slow moving traffic (Gauld et al., ). if drivers were prevented from using their phones in less complex road traffic conditions, they might use their phone in riskier conditions at a later stage.…”

Section: Discussionsupporting

confidence: 84%

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Should I Text or Call Here? A Situation‐Based Analysis of Drivers’ Perceived Likelihood of Engaging in Mobile Phone Multitasking

et al. 2018

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This study investigated how situational characteristics typically encountered in the transport system influence drivers' perceived likelihood of engaging in mobile phone multitasking. The impacts of mobile phone tasks, perceived environmental complexity/risk, and drivers' individual differences were evaluated as relevant individual predictors within the behavioral adaptation framework. An innovative questionnaire, which includes randomized textual and visual scenarios, was administered to collect data from a sample of 447 drivers in South East Queensland-Australia (66% females; n = 296). The likelihood of engaging in a mobile phone task across various scenarios was modeled by a random parameters ordered probit model. Results indicated that drivers who are female, are frequent users of phones for texting/answering calls, have less favorable attitudes towards safety, and are highly disinhibited were more likely to report stronger intentions of engaging in mobile phone multitasking. However, more years with a valid driving license, self-efficacy toward self-regulation in demanding traffic conditions and police enforcement, texting tasks, and demanding traffic conditions were negatively related to self-reported likelihood of mobile phone multitasking. The unobserved heterogeneity warned of riskier groups among female drivers and participants who need a lot of convincing to believe that multitasking while driving is dangerous. This research concludes that behavioral adaptation theory is a robust framework explaining self-regulation of distracted drivers.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 84%

Should I Text or Call Here? A Situation‐Based Analysis of Drivers’ Perceived Likelihood of Engaging in Mobile Phone Multitasking

et al. 2018

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“…Metz, Landau, and Just (2014) evaluated the association between passenger presence and distracted driving of adult drivers, and found that experienced drivers tended to engage in secondary tasks less frequently in the presence of a passenger. Kidd et al (2016), on the other hand, found no difference in the prevalence of secondary task engagement among drivers with or without passengers. The classification of secondary tasks and passenger age varied across these studies.…”

Section: Discussionmentioning

confidence: 73%

“…Two previous naturalistic driving studies reported no associations between the prevalence of secondary task engagement and characteristics of the driving environment (e.g., road surface conditions, time of drive; Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006; Stutts, Reinfurt, & Rodgman, 2001). In contrast, data from a roadside observational study indicated that the prevalence of secondary task engagement was greater in less demanding driving environments (Kidd, Tison, Chaudhary, McCartt, & Casanova-Powell, 2016). However, little else is known about driving environment characteristics and teenage drivers′ engagement in secondary tasks while driving.…”

Section: Introductionmentioning

confidence: 99%

Teens' distracted driving behavior: Prevalence and predictors

Gershon

Zhu

Klauer

et al. 2017

Journal of Safety Research

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Introduction: Teen drivers′ over-involvement in crashes has been attributed to a variety of factors, including distracted driving. With the rapid development of in-vehicle systems and portable electronic devices, the burden associated with distracted driving is expected to increase. The current study identifies predictors of secondary task engagement among teenage drivers and provides basis for interventions to reduce distracted driving behavior. We described the prevalence of secondary tasks by type and driving conditions and evaluated the associations between the prevalence of secondary task engagement, driving conditions, and selected psychosocial factors. Methods: The private vehicles of 83 newly-licensed teenage drivers were equipped with Data Acquisition Systems (DAS), which documented driving performance measures, including secondary task engagement and driving environment characteristics. Surveys administered at licensure provided psychosocial measures. Results: Overall, teens engaged in a potentially distracting secondary task in 58% of sampled road clips. The most prevalent types of secondary tasks were interaction with a passenger, talking/singing (no passenger), external distraction, and texting/dialing the cell phone. Secondary task engagement was more prevalent among those with primary vehicle access and when driving alone. Social norms, friends’ risky driving behaviors, and parental limitations were significantly associated with secondary task prevalence. In contrast, environmental attributes, including lighting and road surface conditions, were not associated with Teens′ engagement in secondary tasks. Conclusions: Our findings indicated that teens engaged in secondary tasks frequently and poorly regulate their driving behavior relative to environmental conditions. Practical applications: Peer and parent influences on secondary task engagement provide valuable objectives for countermeasures to reduce distracted driving among teenage drivers.

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“…Studies have shown that drivers successfully balance the attentional resources between driving and a secondary tasks using compensatory behavior, e.g. lowering driving speed or pausing conversation in a demanding driving situation [2–4]. …”

Section: Introductionmentioning

confidence: 99%

Task-Difficulty Homeostasis in Car Following Models: Experimental Validation Using Self-Paced Visual Occlusion

et al. 2017

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Car following (CF) models used in traffic engineering are often criticized for not incorporating “human factors” well known to affect driving. Some recent work has addressed this by augmenting the CF models with the Task-Capability Interface (TCI) model, by dynamically changing driving parameters as function of driver capability. We examined assumptions of these models experimentally using a self-paced visual occlusion paradigm in a simulated car following task. The results show strong, approximately one-to-one, correspondence between occlusion duration and increase in time headway. The correspondence was found between subjects and within subjects, on aggregate and individual sample level. The long time scale aggregate results support TCI-CF models that assume a linear increase in time headway in response to increased distraction. The short time scale individual sample level results suggest that drivers also adapt their visual sampling in response to transient changes in time headway, a mechanism which isn’t incorporated in the current models.

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The influence of roadway situation, other contextual factors, and driver characteristics on the prevalence of driver secondary behaviors

Cited by 61 publications

References 20 publications

Should I Text or Call Here? A Situation‐Based Analysis of Drivers’ Perceived Likelihood of Engaging in Mobile Phone Multitasking

Should I Text or Call Here? A Situation‐Based Analysis of Drivers’ Perceived Likelihood of Engaging in Mobile Phone Multitasking

Teens' distracted driving behavior: Prevalence and predictors

Task-Difficulty Homeostasis in Car Following Models: Experimental Validation Using Self-Paced Visual Occlusion

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