• Korean Journal of Culture and Social Issue
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• v.18 no.3
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• pp.415-433
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• 2012

This study was conducted in order to explain the effect of driving behavior determinants such as drivers' personality and attitude that may induce risky driving behavior and to develop a valid method for discriminating risky drivers using the determinants. In the results of surveying 534 adult drivers, 5 driving behavior determinants (avoidance of problems, benefit/stimulus seeking, interpersonal anxiety, interpersonal anger, and aggression) were found to have a statistically significant effect on drivers' various risky driving behaviors. Using these factors, drivers were grouped according to risk levels (normal drivers, unintentionally risky drivers, and intentionally risky drivers). This result suggests that drivers' dangerous behavior level can be predicted using psychological factors such as their personality and attitude. Accordingly, if the driving behavior determinant model and the base score system used in this study are improved through further research, they are expected to be useful in predicting drivers' recklessness in advance, identifying problems, and providing differentiated safe driving education services based on the results.

• Korean Journal of Health Education and Promotion
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• v.2 no.1
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• pp.115-132
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• 2000

The purpose of this study is to provide baseline information on the risk-taking health behavior of alcohol consumption in four ethnic groups, Caucasian, Chinese, Japanese, and Korean, residing in the State of Hawaii. Secondary data from the State-based Health Behavioral Risk Factor Surveillance System, designed by the Center for Disease Control, were used. The total sample analyzed for this study contained 6,068 persons. Univariate and logistic regression analysis were performed in order to determine sociodemographic profiles and the predictor variables to produce the findings of this study. The percentage distribution of six sociodemographic factors by race was very similar in all alcohol consumption factors, acute drinking, chronic drinking, and drinking and driving. In this study there were significant ethnic differences in alcohol consumption factors except drinking and driving.

• International Journal of Highway Engineering
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• v.14 no.4
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• pp.149-162
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• 2012

PURPOSES : The causes of traffic accidents can be classified into the factors of highway users, vehicles, and driving environments. Traffic accidents result from the deficiency in single or combination of these three factors. The objective of this study is to define the "potentially hazardous sections of highway" in terms of traffic safety considering these three factors. METHODS : The test drivers performed repeated driving on these highway sections. The drivers and passengers recorded the sections on which the driving was uncomfortable, and the speeds on the sections excluding the uncomfortable sections were used for the development of the model. RESULTS : The model is composed of three sub-models for each of the horizontal curve, tangent, and the section where the curve starts/ends. The safe driving behavior coefficients by the horizontal curvature were derived by comparing the maximum operating speeds at which the vehicle may slide or deviate and the speeds at which the drivers feel comfort. The safety speeds on tangent were derived by the length of tangent section considering the driver's desired speeds under the traffic condition on which the drivers hardly influenced by the other vehicles. For the sections where the curve starts/ends, the driving behaviors were classified by the distances between the curves, and the safe acceleration/deceleration speeds were derived on which the drivers enter/exit the curve sections safely. CONCLUSIONS : Safety speed could then be regarded that the model suggested in this study may be useful to define the potentially hazardous highway section and contribute the improvement of highway safety.

• International Journal of Highway Engineering
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• v.14 no.1
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• pp.103-109
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• 2012

Digital speedometer which is supposed to provide the basic data for analyzing human factors of drivers has a limitation for human behavior studies of drivers, because it records limited driving information including GPS velocities. Besides, Black Box, which is currently being actively commercialized in the market, records mostly vehicles' risky patterns rather than drivers' behaviors. As a result, it also shows a limit to analyze dangerous driving patterns. This study performed a risky driving study for human factor analysis. This study conducted before and after comparisons for real time warning study using a risky driving judgment device. The analysis was conducted based on Longitudinal acceleration, Lateral acceleration, and Yaw rate of vehicles.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.402-409
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• 2007

Markov Chains has proven to be effective in predicting human behaviors in the areas of web site assess, multimedia educational system, and driving environment. In order to extend an application area of predicting human behaviors using Markov Chains, this study was conducted to investigate whether Markov Chains could be used to predict human behavior in selecting mobile phone menu item. Compared to the aforementioned application areas, this study has different aspects in using Markov Chains : m-order 1-step Markov Model and the concept of Power Law of Learning. The results showed that human behaviors in predicting mobile phone menu selection were well fitted into with m-order 1-step Markov Model and Power Law of Learning in allocating history path vector weights. In other words, prediction of mobile phone menu selection with Markov Chains was capable of user's actual menu selection.

• Knowledge Management Research
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• v.16 no.2
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• pp.249-278
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• 2015

Employees' knowledge integration capabilities and innovative behavior are still of crucial importance in the effective knowledge management. Recently researchers and practitioners are interested in both the potential benefits of unlearning and the negative aspects of learning inertia. The purpose of this study is to examine the relationships among learning inertia, unlearning, knowledge integration capabilities(knowledge exploitation and knowledge exploration) and innovative behavior. The results of analysis show that learning inertia is employees' psychological obstacle factor affecting knowledge integration capabilities and unlearning, that unlearning of employees is a key factor affecting knowledge integration capabilities, and that knowledge integration capabilities are driving forces leading to innovative behaviors of employees. For theoretical and practical implications, the research presents the grounds for arguments that knowledge integration capabilities are employees' dynamic capabilities from the knowledge management perspective, that unlearning is a driving force of employees' positive behaviors, and that organizations trying to perform the dynamic knowledge management need to identify the causes of employees' psychological resistance to learning. Limitations arisen in the course of the research and suggestions for future research directions are also discussed.

• International Journal of Highway Engineering
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• v.17 no.5
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• pp.83-92
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• 2015

PURPOSES : Traffic accidents and damage due to speeding should be recognized as a problem which harms society and the economy as well as the parties to the accidents. It is time to seek more detailed and concrete customized alternatives than the existing policies for the prevention of traffic accidents. METHODS: In this study, we identified the characteristics driver behavior and psychological factors that lead to speeding, and a study was carried out to verify the causality models developed from the factors we identified. RESULTS : Driving behavior variables have a significant effect on speeding behaviors in order of Lapse, Violation, and Mistake. And the violation which is defined as intentional violation showed the result which supports the research hypothesis as it has the significant effect on speeding intention and behaviors. CONCLUSIONS: The result of this study can be utilized to develop educational problems concerning speeding and previous response with the main objective of eliminating speeding driver behavior.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.332-347
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• 2017

Analysis of the interaction between the automated vehicles and manual vehicles is very important in analyzing the performance of automated cooperative driving environments. In particular, the automated vehicle platooning can affect the driving behavior of adjacent manual vehicles. The purpose of this study is to analyze the lane change behavior of the manual vehicles in automated vehicle platonning environment and to conduct the experiment and questionnaire surveys in three stages. In the first stage, a video questionnaire survey was conducted, and responsive behaviors of manual vehicles were investigated. In second stage, the driving simulator experiments were conducted to investigate the lane change behaviors of in automated vehicle platonning environments. To analyze the lane change behavior of the manual vehicles, lane change durations and acceleration noise, which are indicators of traffic flow stability, were used. The driving behavior of manual vehicles were compared across different market penetration rates (MPR) of automated vehicles and human factors. Lastly, NASA-TLX (NASA Task Load Index) was used to evaluate the workload of the manual vehicle drivers. As a result of the analysis, it was identified that manual vehicle drivers had psychological burdens while driving in automated vehicle platonning environments. Lane change durations were longer when the MPR of the automated vehicles increased, and acceleration noise were increased in the case of 30-40 years old or female drivers. The results from this study can be used as a fundamental for more realistic traffic simulations reflecting the interaction between the automated vehicles and manual vehicles. It is also expected to effectively support the establishment of valuable transportation management strategy in automated vehicle environments.

• ETRI Journal
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• v.38 no.4
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• pp.757-766
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• 2016

This study was conducted to examine the usage effects of an augmented reality head-up display (AR-HUD) system on the risk perception and psychological changes of drivers. To do so, we conducted an experiment to collect the driver response times for vehicles and pedestrians as their risk perception behavior, and used a driving behavior determinants questionnaire consisting of Problem Evading, Benefits/Sensation Seeking, Anti-Personal Anxiety, Anti-Personal Angry, and Aggression factors for collecting the psychological characteristics of the drivers. Thirty drivers were randomly assigned into an in-vehicle AR-HUD using group and a control group. As a result, the Anti-Personal Anxiety and Anti-Personal Angry factors were negatively correlated with the response time for the control group. In contrast, these results were not shown for the in-vehicle AR-HUD system using group. These results indicate that the in-vehicle AR-HUD system may partially induce a relaxation of tension or stress for drivers with a high level of interpersonal anxiety. Therefore, the in-vehicle AR-HUD system might contribute to not only the visual safety driving behaviors of drivers, but also to their psychological driving safety with specific characteristics.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.5
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• pp.13-22
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• 2015

These days, human behavior (human factor), the main cause of traffic accidents, has drawn more attention. Research on driving behavior based on DBQ(Driver Behavior Questionnaire), the analysis tool of driving behavior, has been conducted actively. In domestic previous studies, their analysis subjects were limited to researchers or military officials, and their analysis methods were based on factor analysis and regression analysis. Therefore, this study tries to find the factors of general drivers' driving behavior that influence risk driving, and to analyze their influential relationship. Regarding study scope, general drivers with driving career were asked to answer DBQ questionnaire, and 300 effective samples were analyzed. In addition, previous studies were investigated to draw the three measurable attributes of DBQ-'Lapse, Mistake, and Violation'-as main factors of traffic accidents, and structural equation model was applied to design risk driving behavior model. To identify the difference between risk driving groups, this study made use of multiple group analysis. The analysis came to the following results: First, according to the examination of the hypothesis that 'Lapse, Mistake, and Violation factors will influence risk driving behavior', all factors were found to be statistically significant. Regarding their level of influence on risk driving behavior, Violation was 0.464, Lapse 0.383, and Mistake 0.158, and thus Violation was analyzed to be the most influential. Secondly, according to the examination of the hypothesis that 'the influence of Lapse, Mistake, and Violation factors on risk driving behavior will be different by risk group', the influence of Lapse on risk driving behavior was found to be different by risk group. It is expected that the study results will be used as a fundamental program to introduce traffic accident prevention program and education that takes violation and lapse into consideration.