What contextual and demographic factors predict drivers’ decision to engage in secondary tasks?

Young, Kristie Lee; Osborne, Rachel; Charlton, Judith Lynne; Grzebieta, Raphael; Williamson, Ann; Haworth, Narelle; Woolley, Jeremy; Senserrick, Teresa

doi:10.1049/iet-its.2018.5546

Cited by 17 publications

(7 citation statements)

References 16 publications

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“…More generally, the finding that the implementation intention intervention reduced mobile phone use in specified driving situations only means that there is no evidence for any unintended detrimental effects of the current intervention on behaviour. Specifically, if the implementation intention intervention caused drivers to transfer their mobile phone use from one situation to another (Huth et al, 2015;Oviedo-Trespalacios, 2018 andYoung et al, 2019), then an increase in mobile phone use in the unspecified situations would have been expected. However, that was not the case.…”

Section: Support For the Hypothesis And Contextualisation Of The Find...mentioning

confidence: 99%

Evidence that implementation intentions reduce drivers’ use of mobile phones while driving

Elliott

Paterson

Orr

et al. 2021

Transportation Research Part F: Traffic Psychology and Behaviou

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Section: Support For the Hypothesis And Contextualisation Of The Find...mentioning

confidence: 99%

Evidence that implementation intentions reduce drivers’ use of mobile phones while driving

Elliott

Paterson

Orr

et al. 2021

Transportation Research Part F: Traffic Psychology and Behaviou

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“…The Brazilian value of 7.03% of time using the mobile phone (I 3 ) is similar in comparison to the Australian value of 7% [ 22 ]. Although the average value found in this research for I 3 is lower than the most recent Netherlands study, 9.2% [ 17 ], it is higher than the Swedish value, 0.63% [ 19 ], the North American values, 2.3% [ 13 ] and 6% [ 31 ], as well as the older Netherlands study with 4% [ 16 ].…”

Section: Discussionmentioning

confidence: 81%

“…In Sweden, data collected also in the EuroFOT project from 100 drivers for one year was analyzed [ 19 , 20 ]. In Australia, data from the Australian Naturalistic Driving Study (ANDS) included 377 drivers monitored over 4 months [ 22 ]. In the SHRP 2 project (Second Strategic Highway Research Program Naturalistic Driving Study), data from 3262 drivers was examined in the United States [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

“…The lack of reliable data on MPU while driving imposes important barriers to evidence-based decision making in LMICs. To address this shortcoming, the prevalence and risks of MPU while driving have been increasingly studied using naturalistic methods in countries such as the United States [ 12 , 13 , 14 , 15 ], the Netherlands [ 16 , 17 ], Germany [ 18 ], Sweden [ 19 , 20 ], Finland [ 21 ], Australia [ 22 ] and China [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Naturalistic Driving Study in Brazil: An Analysis of Mobile Phone Use Behavior while Driving

Bastos

Santos

Amancio

et al. 2020

IJERPH

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Mobile phone use (MPU) while driving is an important road safety challenge worldwide. Naturalistic driving studies (NDS) emerged as one of the most sophisticated methodologies to investigate driver behavior; however, NDS have not been implemented in low- or middle-income countries. The aim of this research is to investigate MPU while driving and compare the results to those reported in international studies. An analysis of 61.32 h and 1350 km driven in Curitiba (Brazil) showed that MPU lasted for an average of 28.51 s (n = 627) and occurred in 58.71% of trips (n = 201) with an average frequency of 8.37 interactions per hour (n = 201). The proportion of the trip time using a mobile phone was 7.03% (n = 201), and the average instantaneous speed was 12.77 km/h (n = 627) while using the phone. Generally, drivers spent less time on more complex interactions and selected a lower speed when using the phone. MPU was observed more during short duration than longer trips. Drivers in this study engaged in a larger number of MPU compared to drivers from Netherlands and the United States; and the percentage of trip time with MPU was between North American and European values.

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“…Strategic control is not to perform secondary tasks while driving (e.g., the driver may turn off any mobile phones before driving), while tactical control is the management of time spent on secondary tasks (e.g., adjusting the time of use of the mobile phone), and operational control refers to adjusting the driving tasks when the driver is engaged in secondary tasks (e.g., increasing time headway or reducing speed) [14]. Numerous scholars have used this theory to study the mechanism of behavioral adaptation of mobile phone distracted drivers [14,42,43]. The current study is limited to operational control, especially the time headway adaptation behavior of mobile phone distracted drivers.…”

Section: Introductionmentioning

confidence: 99%

Mobile Phone Use in a Car-Following Situation: Impact on Time Headway and Effectiveness of Driver’s Rear-End Risk Compensation Behavior via a Driving Simulator Study

Chen

et al. 2020

IJERPH

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Mobile phone use while driving has become one of the leading causes of traffic accidents and poses a significant threat to public health. This study investigated the impact of speech-based texting and handheld texting (two difficulty levels in each task) on car-following performance in terms of time headway and collision avoidance capability; and further examined the relationship between time headway increase strategy and the corresponding accident frequency. Fifty-three participants completed the car-following experiment in a driving simulator. A Generalized Estimating Equation method was applied to develop the linear regression model for time headway and the binary logistic regression model for accident probability. The results of the model for time headway indicated that drivers adopted compensation behavior to offset the increased workload by increasing their time headway by 0.41 and 0.59 s while conducting speech-based texting and handheld texting, respectively. The model results for the rear-end accident probability showed that the accident probability increased by 2.34 and 3.56 times, respectively, during the use of speech-based texting and handheld texting tasks. Additionally, the greater the deceleration of the lead vehicle, the higher the probability of a rear-end accident. Further, the relationship between time headway increase patterns and the corresponding accident frequencies showed that all drivers’ compensation behaviors were different, and only a few drivers increased their time headway by 60% or more, which could completely offset the increased accident risk associated with mobile phone distraction. The findings provide a theoretical reference for the formulation of traffic regulations related to mobile phone use, driver safety education programs, and road safety public awareness campaigns. Moreover, the developed accident risk models may contribute to the development of a driving safety warning system.

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What contextual and demographic factors predict drivers’ decision to engage in secondary tasks?

Cited by 17 publications

References 16 publications

Evidence that implementation intentions reduce drivers’ use of mobile phones while driving

Evidence that implementation intentions reduce drivers’ use of mobile phones while driving

Naturalistic Driving Study in Brazil: An Analysis of Mobile Phone Use Behavior while Driving

Mobile Phone Use in a Car-Following Situation: Impact on Time Headway and Effectiveness of Driver’s Rear-End Risk Compensation Behavior via a Driving Simulator Study

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