• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.15-28
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• 2016

The purpose of this study is to prepare countermeasures for aggressive driving and road rage which have recently become a hot issue by analysing mechanism of how the driving behavior determinants(personal anger and aggression) and dangerous driving behavior factors(aggressive driving behavior and over-speeding driving behavior, drunk driving behavior, inattentive driving behavior, and inexperience driving behavior) affect aggressive driving and road rage. From the survey conducted by seven branches of the Road Traffic Authority with 351 people who were traffic offenders and drivers who caused car accidents, this study obtained three results as follows. First, seriousness of aggressive driving and road rage and requirements as types of customized educations, proper length of time for education, and contents of education can be understood. Second, specific relation and mechanism between the driving behavior determinants and dangerous driving behavior factors with respect to aggressive driving and road rage can be clearly identified, which helps to set order of priority and weighting of measures for reducing aggressive driving and road rage. Third, countermeasures can be categorized as corporate measures or customized measures through mechanism analysis model of aggressive driving and road rage.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.3
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• pp.16-22
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• 2011

This paper presents the potential of in-vehicle data recorder system for monitoring aggressive driving patterns and providing feedback to drivers on their on road behaviour. This system can detect 10 risky types of drivers' driving patterns such as aggressive lane change, sudden brakes and turns with acceleration etc. Vehicle dynamics simulation and vehicle road test have been performed in order to develop driving pattern recognition algorithms. Recorder systems are installed to 50 buses in a single company. Drivers' driving behaviour are monitored for 1 month. The drivers' risky driving data collected by the system are analyzed. Aggressive lane change in 50km/h below is a cause in overwhelming majority of risky driving pattern.

• Journal of Korean Society of Transportation
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• v.36 no.1
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• pp.51-65
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• 2018

Intentional aggressive driving (IAD) is defined as a hazardous driving event that the aggressive driver intentionally threatens neighbor drivers with abrupt longitudinal and lateral maneuvering. This study developed a methodology for detecting IAD events based on the analysis of interactions between aggressive driver and normal driver. Three major aggressive events including rear-close following, side-close driving, and sudden deceleration were analyzed to develop the algorithm. Then, driving simulation experiments were conducted using a multi-agent driving simulator to obtain data to be used for the development of the detection algorithm. In order to detect the driver's intention to attack, a relative evaluation index (Erratic Driving Index, EDI) reflecting the driving pattern was derived. The derived IAD event detection algorithm utilizes both the existing absolute detection method and the relative detection method. It is expected that the proposed methodology can be effectively used for detecting IAD events in support of in-vehicle data recorder technology in practice.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.28-38
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• 2017

The study is to analyze the aggressive driving behavior in the protected/permissive left turn(PPLT) intersections in Gunsan City. As a result of the logistic regression model, increasing of driver's age and driving experience, non-peak time, no company, sedan and male have a tendency to behave aggressive driving to the opposite vehicles. When the vehicles try to turn the unprotected left in the PPLT intersection, the opposite vehicle drivers recognize them at the aggressive driving behavior if the distance to opposite vehicles is not enough. The relationship between driver characteristics and the distance to the opposite vehicles is analyzed under aggressive driving behavior. increasing of age and company, peak time tend to influence the short distance opposite vehicles while male and higher driving experience the middle and long distance. Sedan has the aggressive possibility to shorter distance opposite vehicles rather than others.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.89-98
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• 2007

Nowadays, aggressive driving is a common driving behavior and it is raised as a dangerous factor for both drivers and passengers. However, it is hard to find studies on aggressive driving in Korea, and even the definition of aggressive behavior is not clearly explained. In this study, the term "aggressive behavior" is defined based on previous studies, and the significance of several psycho-social factors which affect aggressive driving were verified with the 'habit' factor from the Theory of Planned Behavior (TPB). In the pilot study, domestic drivers reported that aggressive behaviors included speeding, traffic light signal violation, and improper passing (cutting drivers off when passing). In this study, controlling the demographic characteristics of these three aggressive behaviors, the authors explored the relationship of the behaviors with TPB factors such as the habit factor. Consequently, in the case of drivers' intentions for speeding and traffic light signal violations, subjective norm, perceived behavioral controllability, and habit were significant factors. In the case of intentions for improper passing, only perceived behavioral controllability and habit were significant. In speeding behavior and traffic light signal violations, only habit was significant besides intention; however, in improper passing, both perceived behavioral controllability and habit were significant besides intention. The authors also tested an alternative model including TPB and habit factors for three types of aggressive driving. Finally, there are several implications for a possible intervention program for aggressive driving, though there are some limitations of the study.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.45-54
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• 2007

Aggressive driving is defined as 'driving behavior which intentionally incurs danger and distress to other drivers and passengers'. It increases the possibility of traffic accidents. Also, it threatens lives of drivers ana passengers and brings social and economic harm. According to previous studies. these aggressive behaviors are affected by not only the personality of drivers but also psychosocial factors, such as attitude, aggressive intention, time pressure, and traffic situations, However, most studies on aggressive behaviors conducted with field studies and surveys suggest correlation, not causal relationship. Therefore, in the current study tue authors measured individual factors-the driver's intention for aggressive driving and manipulated time pressure-to examine the interaction effects. As a result. the difference between conditions was significant depending on the level of aggressive intention and time pressure. These differences were also significant depending on the types of aggressive driving behaviors. In particular, the interaction effect of the driver's intention and time pressure on improper passing (cutting drivers off when passing) was significant. Finally the limitations of the current study and implications or using a car simulator are discussed.

• Journal of Animal Science and Technology
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• v.65 no.4
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• pp.878-889
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• 2023

Animal welfare during transport became an largely issue because of increasing demand for improved animal welfare standards. Most studies on the animal welfare during transportation have concentrated on the atmosphere and the temperature of the truck compartments. Thus, the objective of study was to collect and quantify three axis acceleration and determine the effect of bedding for transporting pigs from farm to slaughterhouse. A total of 2,840 crossbred fattening pigs with a live weight of approximately 115 kg were used. They were raised in the same commercial farms and transported to the same commercial slaughterhouse. A 3×2×2 completely randomized factorial design was used to investigate effects of rubber type bedding (bedding or non-bedding) and two levels of driving style (aggressive or normal) in three different time periods with different outside temperatures. Air temperature treatments were as follow: high temperature ([HT] higher than 24℃); low temperature ([LT] lower than 10℃); normal temperature ([NT] 10℃ to 24℃). In our experiment, pigs transported under aggressive driving style showed lower (p < 0.05) pH and water holding capacity (WHC) than those transported under normal driving style. Pigs transported under normal driving style showed a lower percentage of drip loss (DL) (p < 0.05) than those transported with an aggressive driving style. Also, transported with bedding showed higher (p < 0.05) lying behavior but lower (p < 0.05) sitting behavior than those transported without bedding. Pigs transported under normal driving style showed lower (p < 0.05) cortisol level than those transported under aggressive driving style. In conclusion, aggressive driving style cause acute stress in pigs, while bedding helps alleviate acute stress in pigs during transportation in LT.

• Journal of Korean Society of Transportation
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• v.36 no.3
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• pp.169-183
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• 2018

Aggressive driving leads to a greater crash potential because it threatens surrounding vehicles. This study conducted traffic simulation experiments using driving behavior data obtained from multi-agent driving simulations. VISSIM traffic simulator and surrogate safety assessment model (SSAM) were used to identify the impacts of aggressive driving on traffic stream in terms of safety and operational efficiency. Market penetration rates (MPR) of aggressive driving vehicle, coupled with various traffic conditions, were taken into consideration in analyzing the impacts. As expected, it was identified that aggressive driving vehicles tended to deteriorate the traffic safety performance. From the perspective of operational efficiency, interesting results were observable. Under level of service (LOS) A, B, and C, it was observed that the average travel speed increased with greater MPRs. Conversely, the average travel speed decreased with under LOS D and E conditions. The outcome of this study would be effectively used for developing safety-related policies for reducing aggressive driving behavior.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.6
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• pp.517-523
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• 2010

In this paper, we proposed an algorithm to detect aggressive driving status by analysing six kinds of driving patterns, which was achieved by comparing for the feature vectors using mahalanobis distance. The first step is to construct feature matrix of $6{\times}2$ size using frequency response of the time-series accelerometer data. Singular value decomposition makes it possible to find the dominant eigenvalue and its corresponding eigenvector. We use the eigenvector as the feature vector of the driving pattern. We conducted real experiments using three drivers to see the effects of recognition. Although there exists differences from individual drivers, we showed that driving patterns can be recognized with about 80% accuracy. Further research topics will include the development of aggressive driving warning system by improving the proposed technique and combining with post-processing of accelerometer signals.