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171.
172.
Red-Light-Running (RLR) is the major cause of severe injury crashes at signalized intersections for both China and the US. As several studies have been conducted to identify the influencing factors of RLR behavior in the US, no similar studies exist in China. To fill this gap, this study was conducted to identify the key factors that affect RLR and compare the contributing factors between US and China. Data were collected through field observations and video recordings; four intersections in Shanghai were selected as the study sites. Both RLR drivers and comparison drivers, who had the opportunity to run the light but did not, were identified. Based on the collected data, preliminary analyses were firstly conducted to identify the features of the RLR and comparison groups. It was determined that: around 57% of RLR crossed the stop line during the 0–0.4 second time interval after red-light onset, and the numbers of red light violators decreased as the time increased; among the RLR vehicles, 38% turned left and 62% went straight; and at the onset of red, about 88% of RLR vehicles were in the middle of a vehicle platoon. Furthermore, in order to compare the RLR group and non-RLR group, two types of logistic regression models were developed. The ordinary logistic regression model was developed to identify the significant variables from the aspects of driver characteristics, driving conditions, and vehicle types. It was concluded that RLR drivers are more likely to be male, have local license plates, and are driving passenger vehicles but without passengers. Large traffic volume also increased the likelihood of RLR. However, the ordinary logistic regression model only considers influencing factors at the vehicle level: different intersection design and signal settings may also have impact on RLR behaviors. Therefore, in order to account for unobserved heterogeneity among different types of intersections, a random effects logistic regression model was adopted. Through the model comparisons, it has been identified that the model goodness-of-fit was substantially improved through considering the heterogeneity effects at intersections. Finally, benefits of this study and the analysis results were discussed. 相似文献
173.
The aim of the present study is to develop a driving context specific impulsivity scale. First, a qualitative study was conducted by interviewing 20 individual drivers to develop the scale items based on the definitions of the basic impulsivity dimensions in the literature. Then, a quantitative study with a total of 506 individual drivers to examine the psychometric qualities of the newly developed Impulsive Driver Behavior Scale. In addition, the variance in driver behaviors, namely violations, errors, lapses and positive driver behaviors measured by the Driver Behavior Questionnaire (DBQ; Reason, Manstead, Stradling, Baxter, & Campbell, 1990) explained by the general impulsivity scales in the literature and the newly developed scale were compared. In all the comparisons, Impulsive Driver Behavior Scale explained higher amount of variance than the general impulsivity scales. Finally, it was found that the newly developed driving context specific impulsivity scale had incremental validity over the general impulsivity scales in predicting driver behaviors. 相似文献
174.
Research has reported that the foot-in-the-door technique is effective at increasing helping behavior. However, the effect of this technique on negative social behavior has never been examined. A field experiment was conducted to explore whether this technique could reduce aggressiveness. Drivers waiting at a traffic light were blocked by an experimental car. In the Foot-in-the-door condition, when the traffic light was red, a passerby confederate asked the driver for directions to a well-known store located in the area of the experiment. The confederate then thanked the driver and walked off in the direction indicated. In the control condition, no request was addressed to the car driver. When the traffic light turned green, the experimental car pretended to be blocked by an engine problem. The number of drivers who honked at the target car and the amount of time that elapsed before the drivers responded by honking their horns were the dependent variables. It was found that fewer drivers honked in the Foot-in-the-door condition and drivers who honked displayed their behavior later than those in the control condition. Self-perception theory was used to explain these results. 相似文献
175.
The aim of the present study is to investigate the mediating roles of driving skills in relationship between organizational safety strategies and driver behaviours among driving instructors. Driving skills consist of perceptual-motor skills and safety skills. Driver behaviours are investigated under four factors: violations, errors, lapses, and positive driver behaviours. Participants were 132 driving instructors (108 male and 24 female). In order to measure organizational safety strategies, Organizational Safety Strategies Scale (OSSS) was developed for driving schools. Results of the principal component analyses yielded one-factor solution for OSSS. In order to test the indirect effects of organizational safety strategies on driver behaviours through driving skills, multiple mediation analyses were conducted by entering age and annual mileage as the control variables. As organizational safety strategies were stronger, driving instructors had higher levels of perceptual-motor skills, which resulted in higher violations. On the other hand, as organizational safety strategies were stronger, driving instructors had higher levels of safety skills, which resulted in less violations and lapses. It can be inferred that; organizational stronger safety strategies might have negative influences on road safety through higher perceptual-motor skills; whereas there can be positive influences on road safety through higher safety skills. In addition, both skills are related to organizational safety strategies. Hence, driving schools should consider the asymmetric relationship between perceptual-motor skills and safety skills while improving their safety strategies to decrease violations and lapses. Organizations might also develop interventions to balance the stated skills to increase road safety. 相似文献
176.
Worldwide, smartphone use is a major contributing factor to road crash among young drivers. While young drivers may be aware of their heightened crash risk and the legal penalties associated with this behaviour, young drivers continue to engage with their smartphones. The development of novel interventions targeting this behaviour is therefore crucial. The current 2 × 2 between groups experimental study (N = 153, 107F, 43 M, 1 other) investigated the concept of cognitive dissonance in relation to smartphone use among young drivers aged 17–25 years (Mage = 20.66 SD = 2.26). Specifically, it applied the induced hypocrisy paradigm to this context. The induced hypocrisy paradigm elicits cognitive dissonance by asking participants to both advocate for the desired behaviour and identify their engagement in the undesired behaviour. Participants are then motivated to change their behaviour to reduce the feelings of dissonance. The current study investigated the efficacy of both the traditional in-person methodology with a new online methodology. Analyses (e.g., ANCOVA) found that the online conditions were more effective than the in-person groups at eliciting dissonance and that the intervention conditions were more effective in reducing both intention and change in behaviour (from pre- to post-intervention) than the control groups. The intervention groups were also more likely to take/request a flyer about driver distraction. While more research is needed to corroborate these findings, these initial results suggest that cognitive dissonance occurs when young drivers use their smartphones and that the induced hypocrisy paradigm may be an effective intervention. In particular, this study’s findings suggest that an online version of the induced hypocrisy paradigm has merit and may form part of future cost-effective, mass interventions. 相似文献
177.
This study evaluated the power and sensitivity of several core driver workload measures in order to better understand their use as a component of future driver distraction potential evaluation procedures of the in-vehicle human machine interface (HMI). Driving is a task that requires visual, manual and cognitive resources to perform. Secondary tasks, such as mobile phone use and interaction with in-built navigation, which load onto any of these three processing resources increase driver workload and can lead to impaired driving. Because workload and distraction potential are interrelated, a comprehensive method to assess driver workload that produces valid and predictive results is needed to advance the science of distraction potential evaluation. It is also needed to incorporate into New Car Assessment Program (NCAP) testing regimes. Workload measures of cognitive (DRT [Detection Response Task] Reaction Time), visual (DRT Miss Rate), subjective (NASA-TLX [driver workload questionnaire]), and temporal demand (Task Interaction Time) were collected as participants drove one of 40 vehicles while completing a variety of secondary tasks with varying interaction requirements. Of the evaluated measures, variance and power analyses demonstrated that Task Interaction Time is the most sensitive in detecting differences in driver workload between different in-vehicle HMIs, followed by DRT Miss Rate, NASA-TLX and finally DRT Reaction Time. There were relatively weak correlations between each of the four measures. These results suggest that Task Interaction Time, coupled with a reliable visual demand metric such as DRT Miss Rate, eye glance coding, or visual occlusion, more efficiently detect differences in driver workload between different HMIs compared to DRT Reaction Time and the NASA-TLX questionnaire. These results can be used to improve the understanding of the utility of each of these core driver workload measures in assessing driver distraction potential. 相似文献
178.
Vehicles are increasingly equipped with sensors that capture the state of the driver, the vehicle, and the environment. These developments are relevant to formal driver testing, but little is known about the extent to which driving examiners would support the use of sensor data in their job. This semi-structured interview study examined the opinions of 37 driving examiners about data-driven assessment of test candidates. The results showed that the examiners were supportive of using data to explain their pass/fail verdict to the candidate. According to the examiners, data in an easily accessible form such as graphs of eye movements, headway, speed, or braking behavior, and color-coded scores, supplemented with camera images, would allow them to eliminate doubt or help them convince disagreeing test-takers. The examiners were skeptical about higher levels of decision support, noting that forming an overall picture of the candidate’s abilities requires integrating multiple context-dependent sources of information. The interviews yielded other possible applications of data collection and sharing, such as selecting optimal routes, improving standardization, and training and pre-selecting candidates before they are allowed to take the driving test. Finally, the interviews focused on an increasingly viable form of data collection: simulator-based driver testing. This yielded a divided picture, with about half of the examiners being positive and half negative about using simulators in driver testing. In conclusion, this study has provided important insights regarding the use of data as an explanation aid for examiners. Future research should consider the views of test candidates and experimentally evaluate different forms of data-driven support in the driving test. 相似文献
179.
The aim of this study was to analyze and compare the effects of different types of digital billboard advertisements (DBAs) on drivers’ performance and attention allocation. Driver distraction is a major threat to driver safety. DBAs are one form of distraction in drivers’ outside environment. There are many different types of DBAs, such as static images, changing images, or videos. However, it is not clear to what extent each of these contributes to driver distraction. A total of 100 students participated in a controlled driving simulator experiment in an urban environment. Measures of driving performance were collected, as well as eye tracking and EEG as windows into attention allocation. The different types of DBAs investigated were static (a single image), transitioning (one static DBA replaces another), and animated (short videos). The statistical analysis demonstrated that there were significant differences in the effect of each type of DBA on drivers' performance (deviation from the center of the lane and reaction time), visual attention to the road (percent of fixations on the road, percent of fixations on DBAs, fixation duration on DBAs, and number of gazes on DBAs), and the EEG theta band and beta band. These results show that driving performance and attention to the road were both more negatively affected when drivers were exposed to transitioning and animated DBAs as compared to static DBAs. The results of this study provide guidance for the better design and regulation of DBAs in order to minimize driver distraction. 相似文献
180.
Driver support features (DSF) have the potential to improve safety, but they also change the driver-vehicle relationship —as well as their respective roles and responsibilities. To maximize safety, it is important to understand how drivers’ knowledge and understanding of these technologies—referred to as drivers’ mental models—impact performance and safety. This simulator study examined how drivers with different mental models of adaptive cruise control performed in edge cases. The study compared the responses of groups of drivers, with strong and weak mental models of ACC, established through a combination of screening, training, and exposure, in edge case situations in a high-fidelity driving simulator. In general, participants with strong mental models were faster than those with weak mental models to respond in edge-case situations—defined as cases where the ACC did not detect an approaching object, such as a slow-moving motorcycle. The performance deficits observed for drivers with weak mental models appear to reflect uncertainty surrounding how ACC will behave in edge cases. 相似文献