• Proceedings of the Korean Geotechical Society Conference
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• 2002.11a
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• pp.27-52
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• 2002

Pile driving formula, wave equation analysis of pile driving(WEAP) and dynamic pile loading test have been known to useful tools to appraise the behaviour of pile driving. This paper reviews basic theories of three methods and gives some suggestions to apply them to practice. And also some cases on application of the methods to the sites are discussed in this paper. It appears that it is inevitable for engineers to be experienced well so that the methods can be regarded as useful tools.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.371-377
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• 2012

Objective: The aim of this study is to investigate effect of driver's cognitive distraction on driver's physiological state and driving performance, and then to determine parameters appropriate for detecting the cognitive distraction. Background: Driver distraction is a major cause of traffic accidents and poses a serious threat to traffic safety due to ever increasing use of in-vehicle information systems and mobile phones during driving. Cognitive distraction, among four different types of distractions, prevents a driver from processing traffic information correctly and adapting to change in surround vehicle behavior in time. However, the cognitive distraction is more difficult to detect because it normally does not involve significant change in driver behavior. Method: A full-scale driving simulator was used to create virtual driving environment and situations. Participants in the experiment drove the driving simulator in three different conditions: attentive driving with no secondary task, driving and conducting secondary task of adding numbers, and driving and conducting secondary task of conversing with an experimenter. Parameters related with driver's physiological state and driving performance were measured and analyzed for their change. Results: The experiment results show that driver's cognitive distraction, induced by secondary task of addition and conversation during driving, increased driver's cognitive workload, and indeed brought change in driver's physiological state and degraded driving performance. Conclusion: The galvanic skin response, pupil size, steering reversal rate, and driver reaction time are shown to be statistically significant for detecting cognitive distraction. The appropriate combination of these parameters will be used to detect the cognitive distraction and estimate risk of traffic accidents in real-time for a driver distraction warning system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.26-41
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• 2024

To ensure safer road conditions, understanding the human factors influencing driving behavior is crucial. However, there are many difficulties in deriving the characteristics of individual human factors that affect actual driving behaviors. Therefore, this study analyzes self-reported dangerous-driving propensities in order to explore potential correlations with drivers' behaviors. The goal is to propose a method for assessing driving tendencies based on varying traffic scenarios. The study employed a questionnaire to gauge participants' propensity to drive dangerously, utilizing a simulator to analyze their driving behaviors. The aim is to determine any notable connections between dangerous-driving propensity and specific driving behaviors. Results indicate that individuals exhibiting a high propensity for reckless driving, as identified by the Korean DBQ, tend to drive at higher speeds and display more aggressive acceleration patterns. These findings contribute to a potential method for assessing reckless driving drivers.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.1
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• pp.223-233
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• 2014

This study empirically analyzes the relationships among the road and traffic experts' personal characteristics, the driving behavior and factors being expected to have an effect on the satisfaction about roundabout operation. The factors are drawn and grouped through the principle component analysis to clarify driving environment satisfaction on roundabout operation. Each group is named as personal attribute, driving behavior attribute, and satisfaction. After the variables are refined by confirmatory factor analysis, satisfaction model is developed with personal attribute and driving behavior attributes as exogenous variables and roundabout driving awareness and emotion attributes as endogenous variables. As a result, driving satisfaction of roundabout operation is directly influenced by delay reduction, safety improvement, capacity increase, sight improvement, severity accident reduction, and bicycle convenience and indirectly gender, age, driving time, and driving experience. Law obeyance, driving concession, traffic sign obeyance, and interposition do not statistically shows significant on satisfaction. As a result of Analytical Hierarchy Process (AHP), the turning radius of geometry and the driving behavior are important elements for roundabout safety.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.409-419
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• 2013

The purpose of this study was to figure out the structural relations between service quality perceived by golf driving range customers, service value, customer satisfaction, and repurchase behavior. For the subject of this study, we selected 374 persons among customers who used 8 golf driving ranges located at 'P' Metropolitan city, 'U' Metropolitan city, or 'C' city in 'K' do through the Convenient Sampling Method. The results were as follows: First, the golf driving range's service quality has a positive effect on the service value. Second, the golf driving range's service quality has a positive effect on the customer satisfaction. Third, the service value has a positive effect on the customer satisfaction. Fourth, the service value has a positive effect on the repurchase behavior. Finally, the customer satisfaction has a positive effect on the repurchase behavior.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.86-94
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• 2015

In recent days, a Connected Car has caught an attention of the motor companies and various industrial institutes such as communication company. An automobile is regarded as a device and has been developed as an interactive system because the system is connected with various device. This drives a new business item, too. As a new automatic car technology is emerging, a new type of car accident is appeared, too. So, many researches for preventing car accident comes from the driver's are carried out in many car related institutes. In this paper, we study a driver's driving workload and develop an algorithm that measures the driver's driving behavior. We can see that the developed algorithm runs well by the experiment of road test. This results affects various road condition, driver's driving behavior and load that reflects the driver's status.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.4
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• pp.35-41
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• 2011

The purpose of this study was to examine drivers' driving behaviors and eye-movements according to driving speed and navigation- position while operation of the navigation in driving. For this purpose, two driving conditions (low-speed and high-speed) and two navigation-positions (top and bottom location of the center console) were set. Drivers' driving behaviors (speed, speed variation, coefficient of variation, and the number of collisions) and eye-movements (overall eye pattern, the average scanning time of navigation, and the number of gaze-out on the road for more 2 seconds) were measured. As a result, when the navigation was located at the bottom of the console, difficulties of lateral control was appeared in low-speed driving condition, and the that of longitudinal control was appeared in high-speed driving condition. In addition, above situation made the drivers' scanning times of navigation long, increased the number of gaze-out on the road for more 2 seconds, and made overall eye pattern monotonous. These results could be interpreted that the manipulation of the navigation at the bottom of console cause reduced attention capacity due to the cognitive load.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.690-696
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• 2011

A number of conventional railway bridge is more than 2600. Non-ballast plate girder bridge is about 700 and this is 27% of all bridge numbers. Non-ballast plate girder has advantages that self load is more lighter than moving load and construction cost is more inexpensive than concrete bridge. But non-ballast plate girder has disadvantages that vibration and noise is bigger than concrete bridge. This study had analyzed behavior of non-ballast plate girder according to the arrangement of supports and driving conditions to review the proper arrangement of support. Measurements were performed in single line and disel locomotive of 7400type were used as test vehicle. The vehicle's driving conditions are as follows; Change of driving direction, Constant speed driving, Deceleration driving, Acceleration driving. Main measurement contents were horizontal displacement and vertical vibration acceleration in girder of vicinity support. Results of measurement are as follows; In case that a vehicle drives from fixed support to movable support, vertical vibration acceleration of the girder was smaller than opposition case.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• International Journal of Highway Engineering
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• v.17 no.2
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• pp.107-122
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• 2015

PURPOSES : The use of virtual driving tests to determine actual road driving behavior is increasing. However, the results indicate a gap between real and virtual driving under same road conditions road based on ergonomic factors, such as anxiety and speed. In the future, the use of virtual driving tests is expected to increase. For this reason, the purpose of this study is to analyze the gap between real and virtual driving on same road conditions and to use a calibration formula to allow for higher reliability of virtual driving tests. METHODS : An intelligent driving recorder was used to capture real driving. A driving simulator was used to record virtual driving. Additionally, a virtual driving map was made with the UC-Win/Road software. We gathered data including geometric structure information, driving information, driver information, and road operation information for real driving and virtual driving on the same road conditions. In this study we investigated a range of gaps, driving speeds, and lateral positions, and introduced a calibration formula to the virtual record to achieve the same record as the real driving situation by applying the effects of the main causes of discrepancy between the two (driving speed and lateral position) using a linear regression model. RESULTS: In the virtual driving test, driving speed and lateral position were determined to be higher and bigger than in the real Driving test, respectively. Additionally, the virtual driving test reduces the concentration, anxiety, and reality when compared to the real driving test. The formula includes four variables to produce the calibration: tangent driving speed, curve driving speed, tangent lateral position, and curve lateral position. However, the tangent lateral position was excluded because it was not statistically significant. CONCLUSIONS: The results of analyzing the formula from MPB (mean prediction bias), MAD (mean absolute deviation) is after applying the formula to the virtual driving test, similar to the real driving test so that the formula works. Because this study was conducted on a national, two-way road, the road speed limit was 80 km/h, and the lane width was 3.0-3.5 m. It works in the same condition road restrictively.