Analysis of Driving Behavior Based on Dynamic Changes of Personality States

Wang, Qingji; Zhang, Junliang; Wang, Shufeng; Li, Sixian; Hou, Wenlan

doi:10.3390/ijerph17020430

Cited by 17 publications

(9 citation statements)

References 43 publications

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“…Allport [26] defined personality traits as the dynamic organization within the individual of those psychophysical systems that determined his unique adjustments to his environment. Personality traits can influence user attitude and behavior [27,28]. Among the related personality traits' papers, the five-factor model has been recognized by most researchers as the most reasonably individual personality model [29].…”

Section: The Influence Of Personality Traits On User Feedbackmentioning

confidence: 99%

The Statistical Analysis of Multidimensional Psychological Characteristics and User Feedback Willingness

Wang

Zhou

et al. 2021

Advances in Mathematical Physics

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The purpose of this paper is to study the influence of multidimensional psychological characteristics on users’ feedback intention by using several statistical analysis methods based on information theory. The feedback process can be described as a communication process based on information theory. The feedback information entropy is associated with the degree of uncertainty elimination of the users who provide feedback information. Many factors are related to this uncertainty, such as information senders often stopped feedback process for some reasons and information senders may have provided fake or spam information. In order to encourage more useful feedback information, a model of a user’s willingness to provide feedback was established with personality traits and cognitive styles as independent variables, feedback motivation as intermediary variables, and feedback willingness as the dependent variable. 206 online and offline questionnaires were obtained to be analyzed by correlation analysis, regression analysis, and structural equation analysis. Cronbach’s α coefficient was used to test the reliability of the questionnaire, and exploratory factor analysis method was used to verify the validity of the questionnaire. First, correlation analysis was used to explore the correlation between personality traits, cognitive styles, and motivation factors. Second, we further explored the strength of the relationship of the five correlated groups of variables through linear regression analysis. At last, we conducted structure equation analyses to test the hypotheses. The results show that both personality traits and cognitive styles can have a significant impact on feedback motivation factors and also show that self-efficacy may be the only evident feedback motivation to encourage useful feedback information. The results show that the willing users with extraversion trait are more likely be motivated by self-efficacy and thus have evident feedback willingness.

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Section: The Influence Of Personality Traits On User Feedbackmentioning

confidence: 99%

The Statistical Analysis of Multidimensional Psychological Characteristics and User Feedback Willingness

Wang

Zhou

et al. 2021

Advances in Mathematical Physics

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“…The user's profile, particularly the personality traits, has also been associated with an effect on driving performance and technology acceptance [44,70,75]. A future study may investigate the association between user personality traits and depression level on the efficacy of the intervention.…”

Section: Discussion and Future Workmentioning

confidence: 99%

AmbientBreath

Lee¹,

Elhaouij²,

Picard³

2021

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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To promote calm breathing inside a car, we designed a just-in-time breathing intervention stimulated by multi-sensory feedback and evaluated its efficacy in a driving simulator. Efficacy was measured via reduction in breathing rate as well as by user acceptance and driving safety measures. Drivers were first exposed to demonstrations of three kinds of ambient feedback designed to stimulate a goal breathing rate: (1) auditory (rhythmic background noise), (2) synchronized modulation of wind (dashboard fans modulating air pointed toward the driver) together with auditory, or (3) synchronized visual (ambient lights) together with auditory. After choosing one preference from these three, each driver engaged in a challenging driving task in a car simulator, where the ambient stimulation was triggered when their breathing exceeded a goal rate adapted to their personal baseline. Two user studies were conducted in a car simulator involving respectively 23 and 31 participants. The studies include both manual and autonomous driving scenarios to evaluate drivers' engagement in the intervention under different cognitive loads. The most frequently selected stimulation was the combined auditory and wind modalities. Measures of changes in breathing rate show that the participants were able to successfully engage in the breathing intervention; however, several factors from the driving context appear to have an impact on when the intervention is or is not effective.

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“…The personality factors, such as the Five-Factor Model ( Costa and McCrae, 1992 ), have been used in numerous studies in relation to risky driving. Earlier studies about extraversion ( Lajunen, 2001 ; Parr et al, 2016 ; Braitman and Braitman, 2017 ; Wang et al, 2018 , 2020 ), neuroticism ( Jovanović et al, 2011 ; Wang et al, 2018 ; Soori and Yousefinezhadi, 2020 ) and openness to experience ( Parr et al, 2016 ; Wang et al, 2020 ) have shown that these personality factors often increase the propensity of various types of risky driving (e.g., distracted driving and aggression) while drivers scoring high in agreeableness and consciousness have been reported to commit risky driving less frequently ( Jovanović et al, 2011 ; Wang et al, 2018 , 2020 ; Hussain et al, 2020 ). When evaluating the effects of personality on risky driving, it is important to bear in mind that those relationships depend on the type of risky driving concerned: rule violations, driver aggression and distracted driving are related to specific personality factors or combinations of those factors.…”

Section: Introductionmentioning

confidence: 99%

Born to Be a Risky Driver? The Relationship Between Cloninger’s Temperament and Character Traits and Risky Driving

Lajunen

Gaygısız

2022

Front. Psychol.

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Temperament refers to basic, largely inherited, relatively stable personality traits which have been present since early childhood. Considering the very fundamental role of temperament in human development and behaviour, it is reasonable to assume that temperament is also related to risky driving and drivers’ view of themselves as drivers. The aim of the present study was to investigate the relationships between Cloninger’s temperament dimensions, risky driving and drivers’ view of their perceptual motor and safety skills. The sample consisted of 335 Turkish drivers (aged 19–57; 53.7% men) who completed an Internet-based survey including Temperament and Character Inventory (TCI), Driver Behaviour Questionnaire (DBQ) and Driver Skill Inventory (DSI). Correlation analyses showed that TCI scale Cooperativeness correlated negatively with all DBQ scales indicating risky driving and positively with safety skills. In regression analyses after controlling age, gender and lifetime mileage, cooperativeness still was significantly related to all DBQ scales and safety skills. Persistence correlated negatively with ordinary violations, lapses and errors and positively with perceptual motor skills. In regression analyses, persistence was related to errors and lapses. Reward dependence was positively related to lapses and harm avoidance negatively to perceptual motor skills. The results of the present study indicate that largely innate temperament character traits may influence an individual’s predisposition to risky driving. Future studies about temperament and risky driving with larger samples allowing sub-group analyses are needed.

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Analysis of Driving Behavior Based on Dynamic Changes of Personality States

Cited by 17 publications

References 43 publications

The Statistical Analysis of Multidimensional Psychological Characteristics and User Feedback Willingness

The Statistical Analysis of Multidimensional Psychological Characteristics and User Feedback Willingness

AmbientBreath

Born to Be a Risky Driver? The Relationship Between Cloninger’s Temperament and Character Traits and Risky Driving

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