scholarly journals An Empirical Study on High-Risk Driving Behavior to Urban-Scale Pattern in China

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 43654-43665
Author(s):  
Nuerzhegeti Aiyitibieke ◽  
Wenjun Wang ◽  
Nannan Wu ◽  
Ying Sun ◽  
Xuewei Li ◽  
...  
Keyword(s):  
High Risk ◽  
Empirical Study ◽  
Driving Behavior ◽  
Risk Driving

scholarly journals Characterization of the Driving Style by State–Action Semantic Plane Based on the Bayesian Nonparametric Approach

2021 ◽  
Vol 11 (17) ◽  
pp. 7857
Author(s):  
Xuqiang Qiao ◽  
Ling Zheng ◽  
Yinong Li ◽  
Yuqing Ren ◽  
Zhida Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Behaviors ◽  
Transition Probabilities ◽  
Driving Behavior ◽  
The State ◽  
Driving Style ◽  
State Action ◽  
Nonparametric Approach ◽  
Driving Behaviors ◽  
Risk Driving

The quantification and estimation of the driving style are crucial to improve the safety on the road and the acceptance of drivers with level2–level3(L2–L3) intelligent vehicles. Previous studies have focused on identifying the difference in driving style between categories, without further consideration of the driving behavior frequency, duration proportion properties, and the transition properties between driving style and behaviors. In this paper, a novel methodology to characterize the driving style is proposed by using the State–Action semantic plane based on the Bayesian nonparametric approach, i.e., hierarchical Dirichlet process–hidden semi–Markov model (HDP–HSMM). This method segments the time series driving data into fragment clusters with similar characteristics and construct the State–Action semantic plane based on the statistical characteristics of the state and action layer to label and interpret the fragment clusters. This intuitively and simply visualizes the driving performance of individual drivers, while the risk index of the individual drivers can also be obtained through semantic plane. In addition, according to the joint mutual information maximization (JIMI) approach, seven transition probabilities of driving behaviors are extracted from the semantic plane and applied to identify driving styles of drivers. We found that the aggressive drivers prefer high–risk driving behaviors, and the total duration and frequency of high–risk behaviors are greater than those of cautious and normal drivers. The transition probabilities among high–risk driving behaviors are also greater compared with low–risk behaviors. Moreover, the transition probabilities can provide rich information about driving styles and can improve the classification accuracy of driving styles effectively. Our study has practical significance for the regulation of driving behavior and improvement of road safety and the development of advanced driver assistance systems (ADAS).

Examining trajectories of adolescent risk factors as predictors of subsequent high-risk driving behavior

2003 ◽  
Vol 32 (3) ◽  
pp. 214-224 ◽  
Author(s):  
Jean T. Shope ◽  
Trivellore E. Raghunathan ◽  
Sujata M. Patil
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Driving Behavior ◽  
Adolescent Risk ◽  
Risk Driving

Adolescent antecedents of high-risk driving behavior into young adulthood: substance use and parental influences

2001 ◽  
Vol 33 (5) ◽  
pp. 649-658 ◽  
Author(s):  
Jean T. Shope ◽  
Patricia F. Waller ◽  
Trivellore E. Raghunathan ◽  
Sujata M. Patil
Keyword(s):  
Substance Use ◽  
High Risk ◽  
Young Adulthood ◽  
Driving Behavior ◽  
Parental Influences ◽  
Risk Driving

Road Rage: User-Reported Antecedents and Potential Solutions

2002 ◽  
Vol 46 (11) ◽  
pp. 920-924
Author(s):  
Tonya L. Smith-Jackson ◽  
Michael S. Wogalter ◽  
Eric F. Shaver
Keyword(s):  
High Risk ◽  
Negative Relationship ◽  
Driving Behavior ◽  
Aggressive Driving ◽  
Road Rage ◽  
Chi Square ◽  
Exact Test ◽  
Data Analyses ◽  
Significant Negative Relationship ◽  
Risk Driving

Road rage (intentional high risk driving behavior) is a factor that increases the likelihood that a driver will be involved in a vehicle crash. The focus of this study was to determine potential antecedents of road rage and methods to prevent road rage. A sample of 372 participants were surveyed. Based upon responses, participant profiles were established to analyze the data. Analyses using Chi-square and Fisher's Exact test revealed a significant negative relationship between age and the tendency toward aggressive driving, particularly tailgating. in addition, content analysis revealed a number of potential antecedents of and solutions to road rage. Human factors implications are discussed.

The Longitudinal Driving Behavior of a Vehicle with Partial Automation: An Empirical Study

2022 ◽  
Author(s):  
Rita Rodrigues ◽  
Ana Bastos Silva ◽  
Luís Vasconcelos ◽  
Álvaro Seco
Keyword(s):  
Empirical Study ◽  
Driving Behavior ◽  
Partial Automation

scholarly journals Satisfaction With Informal Volunteering During the COVID-19 Crisis: An Empirical Study Considering a Swiss Online Volunteering Platform

2020 ◽  
Vol 49 (6) ◽  
pp. 1142-1151
Author(s):  
Stefan Trautwein ◽  
Florian Liberatore ◽  
Jörg Lindenmeier ◽  
Georg von Schnurbein
Keyword(s):  
High Risk ◽  
Empirical Study ◽  
Online Survey ◽  
Risk Groups ◽  
Empirical Results ◽  
Susceptibility To Infection ◽  
Informal Volunteering ◽  
Volunteer Motives ◽  
Online Volunteering

The COVID-19 pandemic has led to a huge wave of compassion. In particular, online volunteering platforms established channeling help for high-risk groups. It is unclear under which conditions volunteers were satisfied with their COVID-19 volunteering mediated by these platforms and whether they will continue their engagement after the crisis. Therefore, and considering personal susceptibility to COVID-19 infection, this study analyzes the effects of different platform support for volunteers and the fulfillment of volunteers’ motives. The study is based on an online survey of a sample of 565 volunteers who registered at and were placed by a Swiss online platform. Fulfillment of distinct volunteer motives and platform support drive COVID-19 volunteering satisfaction. Moreover, motive fulfillment and platform-related support indirectly impact willingness to volunteer long-term via volunteering satisfaction. Finally, the empirical results show that motive fulfillment and the effect of platform support are contingent on perceived susceptibility to infection.

Comparing Software Bugs in Clone and Non-clone Code: An Empirical Study

2017 ◽  
Vol 27 (09n10) ◽  
pp. 1507-1527
Author(s):  
Judith F. Islam ◽  
Manishankar Mondal ◽  
Chanchal K. Roy ◽  
Kevin A. Schneider
Keyword(s):  
High Risk ◽  
Empirical Study ◽  
Software Development ◽  
Comparative Study ◽  
Software Maintenance ◽  
Statistical Significance ◽  
Ongoing Debate ◽  
Software Bugs ◽  
Bug Fixing

Code cloning is a recurrent operation in everyday software development. Whether it is a good or bad practice is an ongoing debate among researchers and developers for the last few decades. In this paper, we conduct a comparative study on bug-proneness in clone code and non-clone code by analyzing commit logs. According to our inspection of thousands of revisions of seven diverse subject systems, the percentage of changed files due to bug-fix commits is significantly higher in clone code compared with non-clone code. We perform a Mann–Whitney–Wilcoxon (MWW) test to show the statistical significance of our findings. In addition, the possibility of occurrence of severe bugs is higher in clone code than in non-clone code. Bug-fixing changes affecting clone code should be considered more carefully. Finally, our manual investigation shows that clone code containing if-condition and if–else blocks has a high risk of having severing bugs. Changes to such types of clone fragments should be done carefully during software maintenance. According to our findings, clone code appears to be more bug-prone than non-clone code.

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