Why do drivers become safer over the first three months of driving? A longitudinal qualitative study

Day, Marianne; Thompson, Andrew R.; Poulter, Damian; Stride, Chris; Rowe, Richard

doi:10.1016/j.aap.2018.04.007

Cited by 34 publications

(13 citation statements)

References 31 publications

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“…Distraction was the smallest theme contributing less than 1% of instruction, 34% were missed HO-DI opportunities, identified by in-vehicle mobile phone alerts, and 42% untaken opportunities. Attention is a critical HO-DS with inattention, or distraction , known to be implicated in young drivers’ risky driving behaviors even during the Learner phase, and lend to increased crash risk ( 16 , 51 – 53 ). The sub-themes, visual, audio , and cognitive are not easily identified by an instructor, nor in observational research.…”

Section: Findings and Discussionmentioning

confidence: 99%

“…Speeding and maintaining safe driving gap events were identified in the study as persistent risky driving behaviors, despite the presence of instructors. It is important to modify these types of behaviors prior to independent driving where they are known to contribute to young drivers’ high crash risk ( 51 – 53 ). Learners’ self-regulation has been shown to be important in reducing engagement in risky driving behaviors ( 16 ).…”

Section: Discussionmentioning

confidence: 99%

“…In general, Learners exhibited difficulties maintaining speed and safe driving gaps evidenced by instructors repeating associated instructions frequently. Research with young novice drivers has revealed these two risky behaviors as persistent and contributing to their increased crash risk (51)(52)(53).…”

Section: Taxonomy Operationalization and Ho-di Dimensionsmentioning

confidence: 99%

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Exploring the Dimensions of Driving Instruction through Naturalistic Observation of Formal Practical Lessons with Learner Drivers

Watson-Brown

Scott‐Parker

Simons‐Morton

et al. 2020

Transportation Research Record

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Higher-order driving skills (HO-DS) are deficient in young drivers who are over-represented in road crash fatalities and serious injuries. Teaching HO-DS has strong theoretical support in reducing crash risk. This study contributes to the dearth of literature regarding on-road driver training that can develop these skills. Higher-order driving instruction (HO-DI) is explored in formal on-road driving lessons (in Queensland, Australia) via naturalistic observation. Fifteen instructors and 96 learner drivers aged 16–19 years were recruited, with 110 lessons observed. An HO-DI coding taxonomy informed by the Goals for Driver Education ( 24) was used for content analysis using an a priori approach, comprising eight HO-DI codes: driving plan (formerly driving route), vehicle control and maneuvering, mastery of traffic situations, surveillance, situational risk (formerly environmental variables), personal risk (formerly knowledge of risk), car function (formerly car knowledge), and distraction. Thirty-nine sub-themes were identified within the codes allowing a deeper understanding of instruction, including missed (unseen) and untaken (seen but not actioned) HO-DI opportunities. The findings inform recommendations for the development of best practice HO-DI. This study has implications for the driver training industry with potential for reducing the crash risk of young novice drivers.

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Section: Findings and Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Exploring the Dimensions of Driving Instruction through Naturalistic Observation of Formal Practical Lessons with Learner Drivers

Watson-Brown

Scott‐Parker

Simons‐Morton

et al. 2020

Transportation Research Record

View full text Add to dashboard Cite

show abstract

“…Bad driving behavior is associated with less driving experience [7]. Day et al discussed the high-risk factors of new drivers through interviews, and put forward suggestions to reduce the risky driving behavior of new drivers [8]. Ma et al studied the causes of the high incidence of accidents among novice drivers, as well as the differences between novice drivers and experienced drivers in terms of risk perception [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Risky Driving Behavior of Novice Drivers

et al. 2019

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Novice drivers have become the main group responsible for traffic accidents because of their lack of experience and relatively weak driving skills. Therefore, it is of great value and significance to study the related problems of the risky driving behavior of novice drivers. In this paper, we analyzed and quantified key factors leading to risky driving behavior of novice drivers on the basis of the planned behavior theory and the protection motivation theory. We integrated the theory of planned behavior (TPB) and the theory of planned behavior (PMT) to extensively discuss the formation mechanism of the dangerous driving behavior of novice drivers. The theoretical analysis showed that novice drivers engage in three main risky behaviors: easily changing their attitudes, overestimating their driving skills, and underestimating illegal driving. On the basis of the aforementioned results, we then proposed some specific suggestions such as traffic safety education and training, social supervision, and law construction for novice drivers to reduce their risky behavior.

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“…Case by case modeling driver’s personal driving behavior is the most straightforward way; however, dangerous driving behavior involves various complex and uncertain factors, such as driving skills, emergency response ability, gender, mood, fatigue, job pressure and even educational background, life experience, etc. ( Horswill and McKenna, 1999 ; Harre and Sibley, 2007 ; Dula et al, 2011 ; Day et al, 2018 ; Fountas et al, 2019 ; Useche et al, 2020 ), thereby making it difficult to directly study personal driving behavior. Nevertheless, during the course of driving, no matter how complex factors the vehicle is subjected to and no matter what driving actions the driver takes, all dangerous driving behaviors will eventually be reflected through the corresponding motion state of vehicle and reaction of passengers on the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network

et al. 2021

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Dangerous driving behavior is the leading factor of road traffic accidents; therefore, how to predict dangerous driving behavior quickly, accurately, and robustly has been an active research topic of traffic safety management in the past decades. Previous works are focused on learning the driving characteristic of drivers or depended on different sensors to estimate vehicle state. In this paper, we propose a new method for dangerous driving behavior prediction by using a hybrid model consisting of cloud model and Elman neural network (CM-ENN) based on vehicle motion state estimation and passenger’s subjective feeling scores, which is more intuitive in perceiving potential dangerous driving behaviors. To verify the effectiveness of the proposed method, we have developed a data acquisition system of driving motion states and apply it to real traffic scenarios in ShenZhen city of China. Experimental results demonstrate that the new method is more accurate and robust than classical methods based on common neural network.

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Why do drivers become safer over the first three months of driving? A longitudinal qualitative study

Cited by 34 publications

References 31 publications

Exploring the Dimensions of Driving Instruction through Naturalistic Observation of Formal Practical Lessons with Learner Drivers

Exploring the Dimensions of Driving Instruction through Naturalistic Observation of Formal Practical Lessons with Learner Drivers

Research on Risky Driving Behavior of Novice Drivers

Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network

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