Aggressive Driving Behavior Measure

2013 ◽  
Author(s):  
Kenneth H. Beck ◽  
Stacey B. Daughters ◽  
Bina Ali
Psycho Idea ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 79
Author(s):  
Muhammad Wika Kurniawan ◽  
Indra Prapto Nugroho

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%.


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

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

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Subramanian Arumugam ◽  
R. Bhargavi

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.


2020 ◽  
Vol 21 (8) ◽  
pp. 3377-3387 ◽  
Author(s):  
Manuel Ricardo Carlos ◽  
Luis C. Gonzalez ◽  
Johan Wahlstrom ◽  
Graciela Ramirez ◽  
Fernando Martinez ◽  
...  

Author(s):  
Talal Al-Shihabi ◽  
Ronald R. Mourant

Autonomous vehicles are perhaps the most encountered element in a driving simulator. Their effect on the realism of the simulator is critical. For autonomous vehicles to contribute positively to the realism of the hosting driving simulator, they need to have a realistic appearance and, possibly more importantly, realistic behavior. Addressed is the problem of modeling realistic and humanlike behaviors on simulated highway systems by developing an abstract framework that captures the details of human driving at the microscopic level. This framework consists of four units that together define and specify the elements needed for a concrete humanlike driving model to be implemented within a driving simulator. These units are the perception unit, the emotions unit, the decision-making unit, and the decision-implementation unit. Realistic models of humanlike driving behavior can be built by implementing the specifications set by the driving framework. Four humanlike driving models have been implemented on the basis of the driving framework: ( a) a generic normal driving model, ( b) an aggressive driving model, ( c) an alcoholic driving model, and ( d) an elderly driving model. These driving models provide experiment designers with a powerful tool for generating complex traffic scenarios in their experiments. These behavioral models were incorporated along with three-dimensional visual models and vehicle dynamics models into one entity, which is the autonomous vehicle. Subjects perceived the autonomous vehicles with the described behavioral models as having a positive effect on the realism of the driving simulator. The erratic driving models were identified correctly by the subjects in most cases.


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