The relationship between drivers’ illusion of superiority, aggressive driving, and self-reported risky driving behaviors

Măirean, Cornelia; Havârneanu, Corneliu-Eugen

doi:10.1016/j.trf.2018.02.037

Cited by 30 publications

(19 citation statements)

References 44 publications

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“…The analysis of dangerous driving behavior mainly focuses on the classification of drivers’ driving styles. Some studies attempt to describe various types of aggressive driving behavior and develop their criteria ( Tasca, 2000 ; Murphey et al, 2009 ; Abou-Zeid et al, 2011 ; Li et al, 2014 ; Carboni and Bogorny, 2015 ; Mãirean and Havãrneanu, 2018 ; Yang et al, 2019 ). In general, the classification algorithms of driving style can be divided into two categories: statistical method and machine learning method.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Some studies analyze traffic accident patterns [12], risk behaviors [13,14], causes of traffic accidents [9,15], types of traffic collisions [8], and time of occurrence [16]. They also determine that the severity of accidents depends on the vehicles involved [17,18] and demographic characteristics [18][19][20][21][22].…”

Section: State Of the Artmentioning

confidence: 99%

Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019

et al. 2021

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Background: Road safety is a significant public health problem because it causes negative consequences on victims and families. The objective was to analyze the most significant changes in traffic crashes in Ecuador during the period from 2000 to 2019. With data obtained from the National Institute of Statistics and Census, we performed the analysis to identify: the number of traffic crashes, the number of victims, and other study variables. Methods: Descriptive and analytical statistics and the contrast of proportions were used to analyze data from 2000 to 2019. Results: According to the ideal joinpoint analysis model, there was a significant decrease in the number of recorded traffic accidents from 2015 to 2019 of −8.54 per year, while the tendency to die increased in females (2.05 per year) and males (3.29 per year). The most common crash was a collision, and the automobile appeared as the most involved vehicle from 2015 to 2019. The hypothesis test contrast is used to determine if statistically significant differences exist between age groups by gender of the driver injured in the period 2017–2018. Conclusions: This study determines the most significant changes in the variables related to traffic crashes, where mortality due to this cause in the last four years has had a growth rate of 1.8% compared to collisions that presented a rate of −31.12%. The contrast of the hypothesis test shows significant differences in the injury level between males and female drivers, depending on the age group.

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“…But, it is not only the great confidence in their abilities, but in a comparative processes with other drivers, they think they are more skilled than other people [84][85][86][87][88]. It is difficult to find a driver who recognizes that he drives very badly.…”

Section: Offender Drivers Profilementioning

confidence: 99%

Cognitive Profile of Optimistic Offender Drivers Affected by Psychological Interventions for a Sustainable and Safer Driving’s Behavior

Valle¹,

Scharifi²

2021

Models and Technologies for Smart, Sustainable and Safe Transportation Systems

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An empirically verified fact is that the majority of traffic accidents occur as a result of risky behaviours that drivers assume, more or less, voluntarily. Drivers are not aware of the perception of risk and the subjective perception of control that we believe we have. We have delimitated the characteristics of a group of optimistic offender drivers, which reveal, on the hand, a great lack of understanding of the true impact that external factors can have on driving and; on the other hand, they tend to overestimate their abilities and overconfident in their ability to avoid accidents. In addition, these drivers do not usually experience negative emotions when they fail. All this, together is what increases the probability of suffering an accident. The consideration of the different cognitive profiles in the perception of the risk or challenge when facing potential traffic situations may provide us with a better understanding of the true nature of offending drivers. The need to carry out experimental studies using new assessment instruments (i.e. Eye tracking, Bio-Feedback, evoked potentials, etc.) can facilitate a better understanding of the cognitive processes that explain the attitudes and behaviors of drivers; and therefore, achieve a lower rate of car accidents.

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The relationship between drivers’ illusion of superiority, aggressive driving, and self-reported risky driving behaviors

Cited by 30 publications

References 44 publications

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

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

Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019

Cognitive Profile of Optimistic Offender Drivers Affected by Psychological Interventions for a Sustainable and Safer Driving’s Behavior

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