Cognitive function and young drivers: The relationship between driving, attitudes, personality and cognition

Zicat, Emma; Bennett, Joanne M.; Chekaluk, Eugene; Batchelor, Jennifer

doi:10.1016/j.trf.2018.03.013

Cited by 63 publications

(34 citation statements)

References 46 publications

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“…Indeed, younger drivers who are involved in crashes also report receiving more traffic offence notices, as well as performing other risky driving behaviours such as drink driving and or speeding (Fergusson, Swain‐Campbell, & Horwood, ; Ivers et al., ). Moreover, drivers’ favourable attitudes towards risky driving behaviours are also associated with more speeding and greater lane deviation during simulated driving tasks (Zicat, Bennett, Chekaluk, & Batchelor, ). To date, little is known about the extent of performing other risky driving behaviours and their association with sleep‐related driving behaviours.…”

Section: Introductionmentioning

confidence: 99%

Young drivers who continue to drive while sleepy: What are the associated sleep‐ and driving‐related factors?

Watling

2019

Journal of Sleep Research

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Crashes due to sleepiness account for a substantial proportion of road crash incidents. The purpose of the current study was to examine several sleep‐related factors and driving‐related factors for their association with self‐reports of continuing to drive while sleepy. In total 257 young drivers aged 18–25 years completed an online survey that assessed factors such as sleep quality, sleep duration and consistency, excessive daytime sleepiness, experiences with sleepiness and their driving‐related behaviours. The results demonstrate that being older, having a perceived ability to overcome sleepiness, committing more highway code violations and having experienced a sleep‐related close call were positively associated with an increased likelihood of continuing to drive while sleepy. The obtained results highlight the acceptance of risky driving behaviours among some younger drivers. Younger drivers’ risky driving behaviour is certainly a road safety concern given the impairment associated with sleepiness and their over‐representation in road crash incidents.

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Section: Introductionmentioning

confidence: 99%

Young drivers who continue to drive while sleepy: What are the associated sleep‐ and driving‐related factors?

Watling

2019

Journal of Sleep Research

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“…They found that the correlation between speed and conscientiousness was significant and consistent with the results of previous questionnaires [31]. Zicat et al characterized driving ability using driving speed indicators on a simulator, and the results showed a significant relationship between the anxious, angry personality traits and the speed of young drivers [32]. Riendeau et al attempted to evaluate the relationship between personality and objective driving results based on simulated driving data.…”

Section: Introductionmentioning

confidence: 53%

Analysis of Driving Behavior Based on Dynamic Changes of Personality States

Wang

Zhang

Wang

et al. 2020

IJERPH

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This study investigated the relationship between personality states and driving behavior from a dynamic perspective. A personality baseline was introduced to reflect the driver’s trait level and can be used as a basic reference for the dynamic change of personality states. Three kinds of simulated scenarios triggered by pedestrian crossing the street were established using a virtual reality driving simulator. Fifty licensed drivers completed the driving experiments and filled in the Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) questionnaire to measure the drivers’ personality baselines. Key indicators were quantified to characterize the five types of personality states by K-means clustering algorithm. The results indicated that the high-risk situation had a greater impact on the drivers, especially for drivers with openness and extroversion. Furthermore, for the drivers of extroverted personality, the fluctuation of personality states in the high-risk scenario was more pronounced. This paper put forward a novel idea for the analysis of driving behavior, and the research results provide a personalized personality database for the selection of different driving modes.

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“…Other vulnerabilities such as driver behaviour, skill, age and adaptability to the hazard (Baker, 1988;Petridou and showing the maximum risk of disruption forecast during January 9, 2015. Blue circles are locations of vehicle overturning events from news media and Twitter; purple circles are events from Highways England; and green circles are travel disruption reports from the weather observation website Moustaki, 2000;Kilpeläinen and Summala, 2007;Zicat et al, 2018), as well as time of day, light conditions (Plainis et al, 2006) and driver tiredness (Williamson et al, 2014) can also play a role and contribute to the risk of a vehicle overturning incident. Asset-user vulnerability was not included in the vulnerability assessment due to the difficulty of acquiring data and the significant challenges associated with modelling human behaviour (Toledo, 2007).…”

Section: Assumptions and Constraints In Quantifying Vulnerability Amentioning

confidence: 99%

Developing a hazard‐impact model to support impact‐based forecasts and warnings: The Vehicle OverTurning (VOT) Model

Hemingway

Robbins

2019

Meteorological Applications

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Impact‐based weather warnings have been issued by the Met Office since 2011. The National Severe Weather Warning Service (NSWWS) uses a risk matrix combining the level of impacts the weather may cause and the likelihood of those impacts occurring to define a warning level. The impact assessment can be considered subjective being predominantly based on meteorologist expertise and experience of past high‐impact weather events and discussions with local advisors. The Vehicle OverTurning (VOT) model is a prototype Hazard Impact Model developed to test the hypothesis that risk models can be run in real time for short‐term weather‐related hazards. The method intends to provide an objective, consistent assessment of potential risk to road users in Great Britain during high‐wind events. It aims to support the production of NSWWS wind warnings by highlighting areas of the road network that are at risk of disruption due to vehicles overturning. The VOT model does this by combining probabilistic hazard information with vulnerability and exposure data to produce a risk of vehicle overturning forecast. The model is being demonstrated with operational meteorologists at the Met Office. Initial reviews of the model's output indicate that the scientific approach used in this prototype can identify routes with a higher risk of vehicle overturning, associated with strong wind events. Ad hoc feedback from forecasters support the authors’ belief that the risk modelling approach is useful to operational meteorologists when issuing weather warnings.

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Cognitive function and young drivers: The relationship between driving, attitudes, personality and cognition

Cited by 63 publications

References 46 publications

Young drivers who continue to drive while sleepy: What are the associated sleep‐ and driving‐related factors?

Young drivers who continue to drive while sleepy: What are the associated sleep‐ and driving‐related factors?

Analysis of Driving Behavior Based on Dynamic Changes of Personality States

Developing a hazard‐impact model to support impact‐based forecasts and warnings: The Vehicle OverTurning (VOT) Model

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