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Method: This qualitative study was a beliefs elicitation study in accordance with the theory of planned behavior and sought to elicit young drivers’ behavioral (i.e., advantages, disadvantages), normative (i.e., who approves, who disapproves), and control beliefs (i.e., barriers, facilitators) that underpin social interactive technology use while driving. Young drivers (N = 26) aged 17 to 25 years took part in an interview or focus group discussion.
Results: Though differences emerged between the 3 behaviors of initiating, monitoring/reading, and responding for each of the behavioral, normative, and control belief categories, the strongest distinction was within the behavioral beliefs category (e.g., communicating with the person that they were on the way to meet was an advantage of initiating; being able to determine whether to respond was an advantage of monitoring/reading; and communicating with important people was an advantage of responding). Normative beliefs were similar for initiating and responding behaviors (e.g., friends and peers more likely to approve than other groups) and differences emerged for monitoring/reading (e.g., parents were more likely to approve of this behavior than initiating and responding). For control beliefs, there were differences between the beliefs regarding facilitators of these behaviors (e.g., familiar roads and conditions facilitated initiating; having audible notifications of an incoming communication facilitated monitoring/reading; and receiving a communication of immediate importance facilitated responding); however, the control beliefs that presented barriers were consistent across the 3 behaviors (e.g., difficult traffic/road conditions).
Conclusion: The current study provides an important addition to the extant literature and supports emerging research that suggests that initiating, monitoring/reading, and responding may indeed be distinct behaviors with different underlying motivations. 相似文献
Methods: Twenty healthy male volunteers undertook 6 driving trials each, 3 in a regular car on a closed track resembling rural road conditions and 3 in a simulator with an identical driving scenario. Ethanol was used as impairing substance due to its well-characterized effects on driving. The subjects were tested sober and at blood alcohol concentrations (BAC) of approximately 0.5 and 0.9 g/L. We explored dose–response relationships between BAC and a range of driving-related measures, as well as their BAC-dependent effect sizes.
Results: In simulator driving, ethanol intake increased steering wheel reversal frequency, steering wheel movement measures, average speed, standard deviation of speed, and pedal use frequency. At the test track, only steering wheel movement and standard deviation of speed were significantly correlated to BAC. Likewise, reaction to unexpected incidents and observance of red traffic lights were adversely affected by ethanol in the simulator but not at the test track. Whereas SDLP showed a relatively large effect size that was similar in simulated and real driving, all other measures demonstrated smaller effect sizes, with less pronounced BAC effects on the test track than in the simulator.
Conclusions: The results suggest that the driving-related measures explored in this study are less sensitive to alcohol-mediated driving impairment than SDLP, especially during real (test track) driving. The discrepancy in effect sizes between simulated and real driving may imply low external validity of these measures in simulator studies. 相似文献
Method: A simulated collision scene was constructed in a driving simulator, and 40 young volunteers (20 male and 20 female) were recruited for tests. Vehicle control parameters and electromyography characteristics of eight muscles of the lower extremity were recorded. The driver reaction time was divided into pre-motor time (PMT) and muscle activation time (MAT). Muscle activation level (ACOL) at the collision moment was calculated and analysed.
Results: PMT was shortest for the tibialis anterior (TA) muscle (243~317 ms for male and 278~438 ms for female). Average MAT of the TA ranged from 28-55 ms. ACOL was large (5~31% for male and 5~23% for female) at 50 km/h, but small (<12%) at 100 km/h. ACOL of the gluteus maximus was smallest (<3%) in the 25 and 100 km/h tests. ACOL of RF of men was significantly smaller than that of women at different speeds.
Conclusions: Ankle dorsiflexion is firstly activated at the beginning of the emergency brake motion. Males showed stronger reaction ability than females, as suggested by male's shorter PMT. The detection of driver's brake intention is upwards of 55ms sooner after introducing the electromyography. Muscle activation of the lower extremity is an important factor for 50 km/h collision injury analysis. For higher speed collisions, this might not be a major factor. The activations of certain muscles may be ignored for crash injury analysis at certain speeds, such as gluteus maximus at 25 or 100 km/h. Furthermore, the activation of certain muscles should be differentiated between males and females during injury analysis. 相似文献
Methods: Analysis of naturalistic driving videos among fleet services drivers for errors and potentially distracting behaviors occurring in the 6 s before crash impact. Categorical variables were examined using the Pearson's chi-square test, and continuous variables, such as eyes-off-road time, were compared using the Student's t-test. Multivariable logistic regression was used to estimate the odds of a driver error or potentially distracting behavior being present in the seconds before rear-end versus angle crashes.
Results: Of the 229 crashes analyzed, 101 (44%) were rear-end and 128 (56%) were angle crashes. Driver age, gender, and presence of passengers did not differ significantly by crash type. Over 95% of rear-end crashes involved inadequate surveillance compared to only 52% of angle crashes (P < .0001). Almost 65% of rear-end crashes involved a potentially distracting driver behavior, whereas less than 40% of angle crashes involved these behaviors (P < .01). On average, drivers spent 4.4 s with their eyes off the road while operating or manipulating their cell phone. Drivers in rear-end crashes were at 3.06 (95% confidence interval [CI], 1.73–5.44) times adjusted higher odds of being potentially distracted than those in angle crashes.
Conclusions: Fleet driver driving errors and potentially distracting behaviors are frequent. This analysis provides data to inform safe driving interventions for fleet services drivers. Further research is needed in effective interventions to reduce the likelihood of drivers' distracting behaviors and errors that may potentially reducing crashes. 相似文献