“…In another study on sensation-seek habit among bus drivers in Karnataka, India [10], high levels of boredom, susceptibility, and disinhibiting were found to be significantly correlated to the probability of dangerous crashes. Another study on the relationship between sensation-seek habit by drivers and involvement in accidents in Iran also revealed similar findings [49]. The study found a significant relationship between sensationseek habit and the probability of making crashes.…”
Section: 4) Sensation-seeksupporting
confidence: 63%
“…The results associated risky behaviors with young drivers. Another study examined critical features associated with car crashes in Iran and linked car drivers of below 20 years of age to sensation-seek feature that resulted in most of the car crashes [49].…”
Driver behavior is a concerning issue in the area of intelligent transportation system (ITS). Driver behavior is a significant key player in a wide range of unpleasant events during the ride, such as accidents or crashes, traffic congestion, harsh braking, and acceleration/deceleration. Influencing factors of driver behavior have been explored in several studies. It is imperative to investigate these factors in order to provide a comprehensive analysis and to categorize them on the basis of a coherent taxonomy. With that, this study conducted a systematic review on prior studies that focused on bus driver behavior, particularly in the ITS. This study also established a taxonomy on the topic of driver behavior in multiple areas of ITS and their classifications. Different databases, namely ScienceDirect, Web of Science, and IEEE Explore, were utilized to obtain relevant articles from 2008 to 2021 (15 April). Several filtering and scanning stages were performed according to the exclusion/inclusion criteria on all 2,803 articles obtained; however, only 87 articles met the criteria. The final set of articles were categorized into a taxonomy. The first part of the taxonomy focuses on five main factors that influence driver behavior: environmental, demographic, habit, vehicle, and on-road routine factors. The second part of the taxonomy discusses the mapping of data collection methods on the basis of four categories: real-time data collection, survey, simulation, and benchmark. Discussion and analysis were provided to highlight the critical literature gaps on bus driver behavior in the ITS, involving the use of real-time data collection, which is imperative for acquiring highly accurate and sophisticated data. This multi-field systematic review has exposed new research opportunities, motivations, challenges, limitations, and recommendations and highlighted the need for the synergistic integration of interdisciplinary works. Overall, this study presented pathways solution in future direction on the basis of three sequenced phases, namely design, labeling and validation, and machine learning. This study can serve as a guide for future researchers, as it addressed the ambiguities in the ITS-driver behavior domain and provided valuable information on these ITS-driver behavior trends.
“…In another study on sensation-seek habit among bus drivers in Karnataka, India [10], high levels of boredom, susceptibility, and disinhibiting were found to be significantly correlated to the probability of dangerous crashes. Another study on the relationship between sensation-seek habit by drivers and involvement in accidents in Iran also revealed similar findings [49]. The study found a significant relationship between sensationseek habit and the probability of making crashes.…”
Section: 4) Sensation-seeksupporting
confidence: 63%
“…The results associated risky behaviors with young drivers. Another study examined critical features associated with car crashes in Iran and linked car drivers of below 20 years of age to sensation-seek feature that resulted in most of the car crashes [49].…”
Driver behavior is a concerning issue in the area of intelligent transportation system (ITS). Driver behavior is a significant key player in a wide range of unpleasant events during the ride, such as accidents or crashes, traffic congestion, harsh braking, and acceleration/deceleration. Influencing factors of driver behavior have been explored in several studies. It is imperative to investigate these factors in order to provide a comprehensive analysis and to categorize them on the basis of a coherent taxonomy. With that, this study conducted a systematic review on prior studies that focused on bus driver behavior, particularly in the ITS. This study also established a taxonomy on the topic of driver behavior in multiple areas of ITS and their classifications. Different databases, namely ScienceDirect, Web of Science, and IEEE Explore, were utilized to obtain relevant articles from 2008 to 2021 (15 April). Several filtering and scanning stages were performed according to the exclusion/inclusion criteria on all 2,803 articles obtained; however, only 87 articles met the criteria. The final set of articles were categorized into a taxonomy. The first part of the taxonomy focuses on five main factors that influence driver behavior: environmental, demographic, habit, vehicle, and on-road routine factors. The second part of the taxonomy discusses the mapping of data collection methods on the basis of four categories: real-time data collection, survey, simulation, and benchmark. Discussion and analysis were provided to highlight the critical literature gaps on bus driver behavior in the ITS, involving the use of real-time data collection, which is imperative for acquiring highly accurate and sophisticated data. This multi-field systematic review has exposed new research opportunities, motivations, challenges, limitations, and recommendations and highlighted the need for the synergistic integration of interdisciplinary works. Overall, this study presented pathways solution in future direction on the basis of three sequenced phases, namely design, labeling and validation, and machine learning. This study can serve as a guide for future researchers, as it addressed the ambiguities in the ITS-driver behavior domain and provided valuable information on these ITS-driver behavior trends.
“…This concern is well reflected in the patient's rights charter (Parsapoor, Bagheri, Larijani, 2009;Rejeh, Heravi-Karimooi, Foroughan, 2010). The term of hospitalization in older adults, due to physiological and anatomical changes (Hajbaghery, Safa, Aminolroayaee-Yamini, 2015;Rahemi et al, 2017), is longer than that of younger age groups (Ferrando et al, 2010). This increases the risk that their dignity will be neglected (Franklin, Ternestedt, Nordenfelt, 2006).…”
Aim: This study was designed to investigate the perceptions of hospitalized older adults and nurses regarding the importance and observance of dignity, and, in addition, to address the relationship between the importance and observance of dignity and individual social variables in nurses and hospitalized older adults, and the relationship between importance and observance of dignity in older adults and hospital wards and bed layouts. Design: A cross-sectional study. Methods: Convenience sampling was used to select 400 hospitalized older adults and 146 nurses in Kashan University of Medical Science hospitals, Iran. Data were collected using a demographic questionnaire, and a dignity scale including three fields: privacy, communication, and autonomy. The data were described using descriptive statistics, and analyzed via the one-way ANOVA, t-test, Pearson correlation coefficient, and linear regression model. Results: Over half of the older adults were female (57%), and 76.5% were married. More than 60% of older adults believed that dignity had a high degree of importance and that observance of dignity was at a good level. The majority of nurses rated the importance and observance of dignity at weak to moderate levels (p < 0.0001). Conclusion: Due to the difference in viewpoints between older adults and nurses regarding dignity, barriers to the observance of dignity as perceived by nurses should be surveyed.
“… 53 For the relationship between SSSS and ARD, the results similar to this study also can be found in previous studies. 54 , 55 Figure 16 Path coefficients from SSSS to SDBS, from SDBS to ARD, and from SSSS to ARD in different SMEs. The path coefficients from SSSS to SDBS, from SDBS to ARD, and from SSSS to ARD in the SMEs applicable to different driving emotions.…”
Purpose
Researches addressing driving behaviors have not fully revealed how emotions affect risky driving behaviors and tend to focus on the effects of some negative emotions rather than those of more specific emotions. This study aimed to test the potential moderating effects of eight common driving emotions on the relationship between self-reported individual traits (sensation seeking and driving style) and actual risky driving behaviors, sequentially providing some implications for the risky driving behavior prevention.
Participants and Methods
A total of 78 licensed drivers were recruited from undergraduate students, company employees and taxi drivers in China. The participants’ data on self-reported driving style (SDBS) and self-reported sensation seeking (SSSS) were obtained through questionnaires. The participants’ data on actual risky driving behaviors (ARD) in eight driving emotional activation states were obtained through a series of emotion induction experiments and driving experiments. The Structural Equation Modeling (SEM) and moderating effect tests were employed to investigate the relationships of driving emotions, SDBS, SSSS and ARD.
Results
Results showed that anger and pleasure affected risky driving behaviors positively by enhancing the relationship between SDBS and ARD, while surprise and fear were negatively related to risky driving behaviors by weakening this relationship. Anxiety positively affected risky driving behaviors by synchronously enhancing the relationship between SDBS and ARD and the relationship between SSSS and ARD, while helplessness and relief affected risky driving behaviors negatively by weakening the two relationships. Contempt affected risky driving behaviors positively by enhancing the relation between SSSS and ARD.
Conclusion
The results illustrated the effects of different emotions on risky driving behaviors, and also partly explained the reasons for these effects. This research provided a source of reference for reducing traffic accidents caused by risky driving behaviors.
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