Predicting aggressive driving behavior: The role of macho personality, age, and power of car

2001 ◽  
Vol 28 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Barbara Krahé ◽  
Ilka Fenske
Keyword(s):  
Driving Behavior ◽  
Aggressive Driving

scholarly journals PERAN SELF CONTROL TERHADAP PERILAKU AGGRESSIVE DRIVING PENGGUNA SEPEDA MOTOR

Psycho Idea ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 79
Author(s):  
Muhammad Wika Kurniawan ◽  
Indra Prapto Nugroho
Keyword(s):  
Young Male ◽  
Sampling Technique ◽  
Driving Behavior ◽  
Self Control ◽  
Aggressive Driving ◽  
Control Scale ◽  
Behavior Scale ◽  
Trial Participants ◽  
Simple Regression

The aim of the study is determining whether there is a role of self control toward aggressive driving behavior on motorcyclist. This study hypothesizes that there is a role of self control toward aggressive driving behavior on motorcyclist. This study used 200 young male motorcyclists in South Sumatera as participants who already has driving license C and used 50 motorcyclists as the trial participants. The sampling technique was purposive sampling. The study measurements are self control scale and aggressive driving behavior scale that refer to Averill’s (1973) self control types and Tasca’s (2000) aggressive driving behavior forms. Data analysis used simple regression.The result of simple regression shows R square = 0,507, F= 203,680, and p = 0,000 (p<0,05). This means that self control has a significant role toward aggressive driving behavior. Thus, the hypothesis could be accepted and self control contribution toward aggressive driving behavior is 50,7%.

scholarly journals Peran Kontrol Diri sebagai Prediktor Perilaku Mengemudi Agresif pada Pengemudi Mobil di Jakarta

MANASA ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 55-64
Author(s):  
Patricia Angeline ◽  
Retha Arjadi
Keyword(s):  
Traffic Accident ◽  
High Speed ◽  
External Factors ◽  
Self Control ◽  
Aggressive Driving ◽  
Driver Behaviour ◽  
Driving Behaviour ◽  
Practical Implications

Many things can cause traffic accident, including the driver behaviour. Aggressive drivingbehaviour is associated with the risk of traffic accident. Aggressive driving behaviour usuallypredicted by external factors, such as other driver’s attitude or gesture that could trigger anger.However, aggressive driving behaviour could also be shown in a situation where there is no otherdriver, for example when someone drive with a high speed in an empty traffic. This means, internalfactor, associated with the ability of the drivers to control themselves, can also contribute toaggressive driving behaviour. This study aims to investigate the role of self-control in predictingaggressive driving behaviour in car driver, specifically in Jakarta. The result from linearaggression analysis shows that self-control significantly predicted aggressive driving behaviourin car drivers in Jakarta. The coefficient is negative, showing that higher self-control determineslower aggressive driving behaviour, and lower self-control determines higher aggressive drivingbehaviour. Practical implications, limitations of the study, and recommendation for future studyare discussed.

scholarly journals On the influence of aggressive driving behavior on the dynamics of traffic flow

PAMM ◽  
2005 ◽  
Vol 5 (1) ◽  
pp. 693-694
Author(s):  
Tilman Seidel ◽  
Ingenuin Gasser ◽  
Gabriele Sirito ◽  
Bodo Werner
Keyword(s):  
Traffic Flow ◽  
Driving Behavior ◽  
Aggressive Driving

Aggressive Driving Behavior and its Distal and Proximal Correlates Among a Group of Traffic Offenders Living in the Greater Tokyo Area

2021 ◽  
Author(s):  
Kazuko Okamura ◽  
Ritsu Kosuge ◽  
Yukako Nakano ◽  
Yutaka Kanno ◽  
Ayaka Ueno ◽  
...  
Keyword(s):  
Driving Behavior ◽  
Aggressive Driving

Aggressive Driving Behavior Measure

2013 ◽  
Author(s):  
Kenneth H. Beck ◽  
Stacey B. Daughters ◽  
Bina Ali
Keyword(s):  
Driving Behavior ◽  
Aggressive Driving

scholarly journals A survey on driving behavior analysis in usage based insurance using big data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Subramanian Arumugam ◽  
R. Bhargavi
Keyword(s):  
Big Data ◽  
Insurance Industry ◽  
Building Blocks ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Road Rage ◽  
Auto Insurance ◽  
Emotional Factors ◽  
The Face ◽  
Pay How You Drive

Abstract The emergence and growth of connected technologies and the adaptation of big data are changing the face of all industries. In the insurance industry, Usage-Based Insurance (UBI) is the most popular use case of big data adaptation. Initially UBI is started as a simple unitary Pay-As-You-Drive (PAYD) model in which the classification of good and bad drivers is an unresolved task. PAYD is progressed towards Pay-How-You-Drive (PHYD) model in which the premium is charged for the personal auto insurance depending on the post-trip analysis. Providing proactive alerts to guide the driver during the trip is the drawback of the PHYD model. PHYD model is further progressed towards Manage-How-You-Drive (MHYD) model in which the proactive engagement in the form of alerts is provided to the drivers while they drive. The evolution of PAYD, PHYD and MHYD models serve as the building blocks of UBI and facilitates the insurance industry to bridge the gap between insurer and the customer with the introduction of MHYD model. Increasing number of insurers are starting to launch PHYD or MHYD models all over the world and widespread customer adaptation is seen to improve the driver safety by monitoring the driving behavior. Consequently, the data flow between an insurer and their customers is increasing exponentially, which makes the need for big data adaptation, a foundational brick in the technology landscape of insurers. The focus of this paper is to perform a detailed survey about the categories of MHYD. The survey results in the need to address the aggressive driving behavior and road rage incidents of the drivers during short-term and long-term driving. The exhaustive survey is also used to propose a solution that finds the risk posed by aggressive driving and road rage incidents by considering the behavioral and emotional factors of a driver. The outcome of this research would help the insurance industries to assess the driving risk more accurately and to propose a solution to calculate the personalized premium based on the driving behavior with most importance towards prevention of risk.

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