Driver Personality Research Articles

자동차대여업자의 운행자성

The contents related to the operator's responsibility under the Automobile Accident Compensation Security Law are virtually the same from the time it was enacted as Law No. 1314 on April 4, 1963. Under these circumstances, the Supreme Court has no choice but to continue its attempts to embody abstract concepts as it extends the concept of existing operating profit and control to ideological concepts. Even if it is understood that it is in line with the legislative purpose of the Automobile Accident Compensation Security Law, it is questionable how long the Supreme Court's interpretation can keep pace with changes in society through technological development. In this judgment, it was questionable whether the vehicle rental company could recognize the driver's personality in the event of an accident due to the tenant's violation, even though the vehicle rental company had restrictions and exemption regulations on the operator of the vehicle as the content of the contract with the tenant. The Supreme Court judges the driver's management of car based on the concept of existing operating profit and operating control (two-way theory), but as the concept of existing operating profit and operating control is abstract, the conclusions of the lower court and the Supreme Court were different. In the end, if the abstract concept of existing operating profit and operation control leads to individual case judgments, there is a risk of reaching unreasonable conclusions in form, and there is a risk of differences in drawing conclusions according to the subjectivity of judges depending on individual cases. If so, it would be more purposeful to interpret the provisions of Article 3 of the Automobile Accident Compensation Security Law to judge the driver's personality based on the subjective sense of responsibility of the party based on an objectified third party.

Simulasi Numerik Model Arus Lalu Lintas dengan Pengaruh Kepadatan Kendaraan dan Kondisi Jalan

Traffic jams are common in developing countries. This is generally caused by traffic density, road conditions, and the driver's personality. This phenomenon can be simulated using a mathematical model, allowing it to be seen how these factors affect traffic conditions. The goal of this study is to simulate the effect of traffic density and road conditions using the Lighthill-Whitham-Richard (LWR) model. This model is used to describe macroscopic traffic flow conditions, which are affected by traffic density and speed. The Greenshield model with the influence of road condition parameters added is used for the speed. In numerical solutions, the Upwind method is used to evaluate midpoint values. The simulation results demonstrate the model's ability to describe traffic conditions that are affected by traffic density and road conditions. The level of traffic density is inversely proportional to road conditions and traffic speed.

Assessing the Possible Effect of Depression on Driving Behavior: A Cross-sectional Study

Background: Driving as a complex behavior pattern is influenced by a set of conscious and unconscious factors. One of the most important causes of traffic accidents in Iran is human factors, which include the driver's mental status, personality traits, mental illnesses, or psychiatric disorders. Depression is one of the most common psychiatric disorders affecting driving behavior. Objectives: The aim of this study was to examine whether depression may affect driving performance. Methods: This cross-sectional study was done on 100 participants who were divided into two groups, patients with major depressive disorder (MDD) and the healthy control group. Driving behavior was assessed by the Manchester Driving Behavior Questionnaire (MDBQ), and three computerized tests [Tower of London Test, Wisconsin Card Sorting Test (WCST), and reaction time] were used to evaluate the participants’ neuropsychiatric characteristics. The results were compared between the two groups using SPSS version 19. Results: There were 51 cases in the MDD group (male = 59%, female = 41%) and 50 patients in the non-depressed group (male = 22%, female = 78%). The mean age of the group with MDD and the control group was 38 ± 8 and 36 ± 9 years, respectively, with no significant differences (P = 0.23). The results showed that the highest mean reaction time (875 ± 198, P = 0.018) was related to the depressed non-risky drivers, and the depressed risky drivers showed the worse function in all domains of WCST (trials to complete first category: 28 ± 23, P = 0.002, total error: 33 ± 9, P = 0.001, and perseveration error: 15 ± 9, P = 0.009) in comparison with another group. Considering the mean score of the tower of London test, the non-depressed non-risky drivers showed the shortest time (219 ± 172, P = 0.001) spent on doing the task, while the depressed non-risky drivers showed the longest mean latency time (213 ± 96, P = 0.001). Conclusions: The findings of the present study showed that depression is associated with deficits in multiple cognitive domains, such as executive function, which may lead to a significant decline in different aspects of driving behavior.

Cognitive selection of driving automation levels in highly automated vehicles leveraging on Bayesian networking principles

Highly automated vehicles (HAVs) are expected to fundamentally change the on-road transportation field by increasing traffic flow efficiency, reducing road crashes caused by human errors, and increasing comfort and driving productivity. With the help of recent developments in cognitive management techniques and machine learning analysis, intelligent on-board computing services are gaining acceptance. The main goal of the present paper is to introduce and develop a novel on-board cognitive decision-making functionality that dynamically and automatically enables HAVs to operate each time in the best available level of driving automation (LoDA). The proposed functionality utilizes several attributes associated with the driving environment and the driver's personality characteristics and personal preferences. The cognitive nature of the proposed functionality is based on previous knowledge, turned into experience, by implementing the Naive Bayes classifier supervised machine learning method. The effectiveness of the proposed cognitive functionality, in terms of accuracy and speed of convergence, in proactively identifying the optimal LoDA, is illustrated by modelling and analysing three scenarios with regards to drivers with different profile data and to driving scenes with different environment characteristics. Therefore, it can operate as an in-car intelligent personal assistant for the drivers.

The association between demographic characteristics, personality, and mental health of bus drivers in China: A structural equation model

BackgroundThe psychological status of drivers is related to adverse driving behaviors. Although there are several studies on the mental health and traffic accidents among bus drivers, related studies on the association between mental health and individual characteristics are scarce. MethodsOur report is on a cross-sectional study of bus drivers that took place between October 2014 and May 2016. A total of 596 bus drivers who worked for transportation companies and underwent occupational physical examinations at The Guangxi Zhuang Autonomous Region workers' Hospital during the study period were included. Demographic characteristics were collected by a self-designed demographic questionnaire. The Eysenck Personality Questionnaire (EPQ) and the Symptom Checklist 90-Revised (SCL-90-R) were used to evaluate the personality and mental health of bus drivers, and the results of the scales were compared with Chinese norms. The structural equation model was used to analyze the correlation among demographic characteristics, personality characteristics, and mental health. ResultsExcept for the lie dimension, the average scores of the driver's personality dimension were higher than the Chinese norm, and the means of somatization and phobia scores of bus drivers were also higher; obsessions-compulsions, interpersonal sensitivity, and depression scores were lower than those for the Chinese norms. The structural equations modeling revealed that the total effects coefficient of demographic characteristics on mental health was 0.68, and that of personality was 0.60. There were direct influences between demographic characteristics and personality characteristics, demographic characteristics and mental health, and personality characteristics and mental health; the standardized path coefficients were 0.60, 0.50, and 0.30, respectively. Smoking, drinking, and neuroticism were the main factors affecting the mental health of bus drivers; and the standardized path coefficients were 0.56, 0.54, and 0.90 respectively. ConclusionsIn this study, bus drivers were significantly more extroverted, psychotic, and neurotic than the general population; and they were more likely to suffer from somatization and phobia. The mental health of bus drivers was associated with some demographic and personality characteristics in the study. These findings provide a theoretical basis for the selection of bus drivers and intervention measures for drivers with high-risk driving tendencies, which is conducive to better guide road traffic safety construction and reduce the occurrence of road traffic injuries.

Fusion Modeling Method of Car-Following Characteristics

Car-following model is indispensable to evaluate the characteristics of car-following behaviors. Through an analysis and comparison of data-driven and theoretically driven car-following models, it shows that the data-driven model has poor interpretability and high quality of training data set is required, while for the theoretical-driven model, it is unable to describe the individualized features and models of the driver so as to a low model accuracy. To solve the problem, a novel modelling method is proposed using adaptive Kalman filter algorithm to integrate the long-short-time memory neural network (LSTM) data-driven model with the IDM theoretical-driven model to build the car-following model. Test results of real driving data from a single driver prove that the fusion car-following model has higher accuracy than a single model, while at the same time highlighting the driver's personality compared to the IDM model. Besides, it improves the generalization ability of the traditional data model, which is reflected by better fitting in the extreme case (for example, the stable state when the acceleration, velocity is zero). Finally, the trajectory simulation results show that the proposed integrated data-driven car-following model can better simulate the micro-traffic behavior of car following.

Универсальные и специфические индивидуально-психологические факторы риска попадания в дорожно-транспортное происшествие у психически здоровых и лиц с психическими расстройствами

The article presents the results of the research aimed to highlight the individual, psychological and cognitive risk factors of getting into a traffic accident depending on the presence of a mental disorder. 139 people aged from 18 to 59 years old (average age 30.44 years ± 9.56) were examined using a set of methods aimed at assessing the individual and typological features of self-control, individual, psychological and cognitive features of the driver's personality. Analysis of the results allowed identifying the universal individual and psychological risk factors for getting into a traffic accident. Specifically: systemic violations of self-regulation in the form of a violation of the connectedness of its stages, impulsiveness, risk-taking, physical aggression. When assessing the contribution of the parameter “guilty / not guilty in accident”, statistically significant differences in the indicators of cognitive methods were revealed. The convicted drivers were significantly distinguished by a high degree of workability, selectivity and accuracy of attention, providing a reactive response, with low processing speed of visual information and fatigue. Characteristics that significantly distinguish the convicted drivers, depending on the presence or absence of the factor “mental disorder”, were also highlighted. Mentally healthy drivers can be characterized by violations of the activation system, due to the orientation of the reward; low level of selectivity of attention, as well as high rates of dissociative driving style, which is characterized by disregard for the rules on the road; low level of planning and evaluation of the results of their own activities. Drivers with mental disorders were statistically significantly distinguished by high rates of risk-taking in the structure of self-control, low level of conscious planning, situational response conditions combined with suspiciousness, irritability, impulsiveness, and a decrease in criticality of mental activity. Characteristic features of their behavior on the road are high rates of anxious driving style, characterized by a heightened sense of alertness and tension while driving. Fatigue and difficulties in workability can be defined among the cognitive variables.

О возможностях прогноза аварийности водителей

The paper describes the approach to solving the problem of the high accident rate on the roads in Russia. The paper points the importance of driving analysis not as an activity but as a behavior, including a multicomponent combined activity of the driver and its interaction with other road users. It is proposed to solve the problem of accidents through conducting of preventive work, in particular, through forecasting the accident drivers. Forecast of accidents to be carried out on the basis of estimates of the ratio of driving style characteristics (determined by personal characteristics of the driver), and professional and important qualities of the driver. The paper sums the results up of the long-term empirical research, in the framework of which was created the questionnaire "Driving style". It was examined the relationship between the inclination of the driver to comply with traffic rules and accidents. It was investigated compensatory possibilities style of activity (such as the characteristics of the driving style) communication between personal (were analyzed accentuation of the driver's personality, lifestyle index, risk sentiment) and stylistic characteristics of the driver, determined by differences in the characteristics of the style of driving accidents and accident-free drivers. The paper gives an approach to the evaluation of driving style as a dangerous, unsafe and safe (to date in respect of the potential). Danger ─ non-hazardous driving style is associated with a propensityaverse of drivers to comply with traffic rules. Safety – insecurity determined by the nature of activity within the style of driving styles depending on whether it is compensation or compensatory nature. Relevance – potentiality of accident-fail-safety of driver is determined based of preserving the reliability of the driver in the different modes of activity.

버스 운전자들의 성격 특성과 안전 직무수행 및 교통사고와의 관계

버스 운전자들의 성격 특성과 안전 직무수행 및 교통사고와의 관계

The Analysis of Driver's Behavior in Non-Signalized Intersection Based on the Game

In order to study the driver's decision-making behaviors of the conflict vehicles in non-signalized intersection, according to time refinement, the driver's personality factors and the relative potential factors in the different strategies which affect the driver to make decisions, Based on the dynamic reduplicate game theory, the utility function of the driver's behaviors was built up. As the decision-making behavior by the driver in the process of cross-road, analyzing the different combination of the utility of the driver's decision-making behavior, Nash equilibrium was existed in a single game process, and the driver's optimal decision behaviors in a dynamic game was obtained. The illustration shows that impulse drivers in the decision-making period of time are more willing to choose to accelerate the first strategy; mild drivers prefer to choose acceleration strategy or uniform strategy; cautious drivers prefer to choose to uniform or deceleration strategy.

Individual driver risk assessment using naturalistic driving data

Driving risk varies substantially among drivers. Identifying and predicting high-risk drivers will greatly benefit the development of proactive driver education programs and safety countermeasures. The objective of this study is twofold: (1) to identify factors associated with individual driver risk and (2) predict high-risk drivers using demographic, personality, and driving characteristic data. The 100-Car Naturalistic Driving Study was used for methodology development and application. A negative binomial regression model was adopted to identify significant risk factors. The results indicated that the driver's age, personality, and critical incident rate had significant impacts on crash and near-crash risk. For the second objective, drivers were classified into three risk groups based on crash and near-crash rate using a K-mean cluster method. The cluster analysis identified approximately 6% of drivers as high-risk drivers, with average crash and near-crash (CNC) rate of 3.95 per 1000miles traveled, 12% of drivers as moderate-risk drivers (average CNC rate=1.75), and 84% of drivers as low-risk drivers (average CNC rate=0.39). Two logistic models were developed to predict the high- and moderate-risk drivers. Both models showed high predictive powers with area under the curve values of 0.938 and 0.930 for the receiver operating characteristic curves. This study concluded that crash and near-crash risk for individual drivers is associated with critical incident rate, demographic, and personality characteristics. Furthermore, the critical incident rate is an effective predictor for high-risk drivers.

An Exploration of Personality and Speed Limit Compliance

Research suggests that noncompliance with speed limits is high. One factor that may provide some explanation for individuals' speeding behavior is the driver's personality. To examine social issue behavior in relation to speeding, this study examined specific personality characteristics in the context of speed limit compliance behavior. To test the hypotheses, data were gathered via an online survey. The results indicate that personality, along with social issue involvement, influenced speed limit compliance behavior.

What have you done for me lately? Performance and brand identification in NASCAR

In endorsement and sponsorship research, the connection between athletes' performance and brand identification is sparse. In the USA, the National Association of Stock Car Auto Racing (NASCAR) is sponsor-driven. While the old line in NASCAR was 'win on Sunday, sell on Monday', the hiring and firing of a driver is often steered by the driver's personality and ability to act in the role of product spokesman. This study uses a two-year sample of 1,000 US fans surveyed by telephone to test whether performance on the track leads to increased visibility of a sponsor. We find that while brand identification is more likely for consistent finishes, driver and sponsor continuity play a more significant role in establishing a link between NASCAR fans and sponsors.

Individual difference factors in risky driving: The roles of anger/hostility, conscientiousness, and sensation-seeking

Motor vehicle crashes claim the lives of more Americans than any other cause of injury. One factor long recognized as relevant to predicting dangerous driver behavior is the driver's personality. This study examines the independent and combined roles of three personality traits – sensation-seeking, conscientiousness, and anger/hostility – in predicting risky driving behavior. Seventy-three participants completed personality and driving history questionnaires, and also engaged in a virtual environment (VE) task designed to assess risk-taking driving behavior. Each facet of personality was correlated to risky driving behavior in independent univariate analyses. In multivariate analyses, sensation-seeking emerged as the best predictor of self-reported driving violations. Anger/hostility and the interactive effect of anger/hostility by sensation-seeking also emerged in a multivariate analysis predicting one measure of self-reported driving violations. No personality trait predicted risky driving in the VE in multivariate analyses. Results are discussed with respect to previous work in the field, challenges involved in measuring the constructs of interest, and implications to prevention.