Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran

Sheykhfard, Abbas; Haghighi, Farshidreza

doi:10.1016/j.jsr.2019.11.002

Cited by 57 publications

(24 citation statements)

References 35 publications

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“…This could result in improper use and unsafe behaviors. Road user behavior was identified as a prominent causal factor in road traffic accidents and was thoroughly investigated [8][9][10]. Unsafe driving behaviors can be defined as a set of implemented and systematic practices that increase the risk of road accidents.…”

Section: Introductionmentioning

confidence: 99%

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

Pulvirenti

Distefano

Leonardi

et al. 2021

Safety

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This study investigated the nature and causes of unsafe driving behavior at roundabouts through an on-road study. Four urban double-lane roundabouts with different layouts were selected for an on-road study. Sixty-six drivers (41 males and 25 females) aged 18–65 years took part in the study. Unsafe behaviors observed during the in situ survey were divided into three different categories: entry unsafe behaviors, circulation unsafe behaviors, and exit unsafe behaviors. Three chi-square automatic interaction detection (CHAID) analyses were developed in order to analyze the influence of roundabout characteristics and maneuvers on unsafe behaviors at double-lane roundabouts. The results confirmed the awareness that double-lane roundabouts are unsafe and inadvisable. More than half of unsafe driving behaviors were found to be entry unsafe behaviors. Moreover, the entry radius was found to be the geometric variable most influencing unsafe driving behaviors.

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Section: Introductionmentioning

confidence: 99%

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

Pulvirenti

Distefano

Leonardi

et al. 2021

Safety

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“…The determine the predictive accuracy of the model, the determination coe cients (R 2 ) of the latent variables were estimated. R 2 values higher than 0.33 are considered acceptable for latent variables (25,26). The R 2 statistics were found to be signi cantly higher than 0.33 ( Table 3) con rming that the model has su cient predictive accuracy.…”

Section: Resultsmentioning

confidence: 88%

Structural Equation Modeling for Identification of Patient Safety Antecedents in Primary Care

Kosiek

Staniec

Godycki-Cwirko

et al. 2020

Preprint

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Background: To create an integrative model of patient safety, concerning antecedents based on the perception of patient safety improvement strategies expressed by primary care professionals: facilities available in the practice, communication and collaboration, education on patient safety and generic conditions for patient safety. Methods: We attended several conferences and seminars in Poland where we presented these strategies. In this way, we gained contacts with doctors potentially interested in patient safety. We ensured ourselves contact persons who could respond to our questionnaire. Those persons assured us that they would send the names of prospective respondents. In this research we were used questionnaire form research Gaal S, Verstappen W, Wensing M (1). The results were analyzed with SPSS 20 and AMOS. A hypothetical model of direct and indirect effects on patient safety in a primary care environment was created and analyzed using structural equation modeling (SEM). Results: Improved implementation of strategies was directly and indirectly associated with better patient safety. SEM proved to be an effective tool to analyse safety in primary care. The facilities in the practice appear to have no significant influence on patient safety management in the case of female respondents, those age lower than mean, current professional discipline is not GP and respondents not work in counselling centres.Conclusions: The following should be improved to ensure patient safety: Generic conditions for patient safety, facilities in the practice, communication and collaboration, and education on patient safety.

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“…Based on accident databases, causes related to vehicle, environment, and road factors affecting the occurrence of accidents have been investigated by researchers through various studies [14]- [23]. Previously, various studies have focused on the causes of accidents, especially human factors as a considerable factor of accidents [4] [5] [6] [7] [17] [20] [21] [22]. For instance, Young et al (2018) reported the possible effects of texting while driving as a human error factor [23].…”

Section: Problem Importance and Research Goalmentioning

confidence: 99%

“…The most remarkable consequence of this need is the increased enthusiasm of people to use personal vehicles during urban and suburban trips, resulting in an abnormal increase in the number and severity of traffic accidents [2] [3] [4]. Generally, the main factors affecting accidents can be classified into four groups: road (geometrical and traffic flow characteristics), vehicles (vehicle ability, technical defects of elements), human factors (driver' characteristics, behaviors, and physiological abilities), and the environment (weather conditions) [5] [6] [7]. These factors interact with each other to affect the occurrence of accidents, and if one of these factors becomes unstable, the possibility of an accident will be increased.…”

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confidence: 99%

“…Previous studies have reported the negative impact of internal distraction factors such as a cellphone[29] [30], or talking to passengers[24], as well as eating and drinking[30] [31] on doing a proper performance by the driver. The effect of external tools, such as traffic signs[32] [33] or digital billboards[7] on driver distraction and the likelihood of an accident has also been reported. The effects of other factors such as using drugs and alcohol on the drivers' behavior such as driving at unauthorized speeds or overtaking have been stated in the previous studies[32] [33].…”

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confidence: 99%

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Analyzing Drivers’ Mental Patterns Using Q-Methodology

Sheykhfard¹,

Haghighi²,

Soltaninejad³

et al. 2020

JTTs

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The human factor is the most important cause of road accidents. Investigating the drivers' mental patterns can lead to a better understanding of the factors that affect drivers to make a mistake and thus increase the likelihood of an accident. In this study, mental patterns of drivers as a human characteristic are determined through a questionnaire survey. To do this, 166 participants (18 -65 years) were asked to express their opinion on the possible effect of 25 factors on the occurrence of accidents. These factors were selected through the investigation of the accident database during the last three years in different areas of the case study. To analyze the data extracted from the survey, Q-methodology was used. The results of the factor analysis showed that there are 5 mental patterns among the participants. Based on the driver's opinion, human factors and road conditions were the most and least influential accident-generating items, respectively. The most significant reason for accidents determined by drivers was human errors including 1) unauthorized overtaking, 2) unauthorized speed, 3) driver distractions (such as cell phone), and 4) driver physical disability (such as visual impairment). Moreover, the failure of the vehicle was mostly reported as another influential contributor to accidents. It is worth mentioning that the results of this study can be used to minimize accidents resulted from drivers' behavioral errors by suggesting strategies for enhancing their performance through new manuals which is a step towards a safer road.

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Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran

Cited by 57 publications

References 35 publications

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

Structural Equation Modeling for Identification of Patient Safety Antecedents in Primary Care

Analyzing Drivers’ Mental Patterns Using Q-Methodology

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