Measuring risky driving behaviours among young drivers: Development of a scale for the Oman setting

Reesi, Hamed Al; Freeman, James E.; Davey, Jeremy D.; Al-Adawi, Samir; Maniri, Abdullah Al

doi:10.1016/j.trf.2018.02.030

Cited by 23 publications

(22 citation statements)

References 35 publications

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“…It had a direct effect on crash risk probability with a path coefficient equal to 0.4431. Speeding is considered one of the main exposures that increase crash risk probability [51,52,55]. Dangerous driving behavior was found to have a direct effect on rash driving behavior and crash risk probability latent variables with a path coefficient of 0.3143 and 0.1114, respectively.…”

Section: Sem Resultsmentioning

confidence: 99%

Investigating in-vehicle distracting activities and crash risks for young drivers using structural equation modeling

Gaweesh

Ahmed

2020

PLoS ONE

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Distracted driving has been considered one of the main reasons for traffic crashes in recent times, especially among young drivers. The objectives of this study were to identify the distracting activities in which young drivers engage, assess the most distracting ones based on their experiences, and investigate the factors that might increase crash risk. The data were collected through a self-report questionnaire. Most participants reported frequent cell phone use while driving. Other reported activities include adjusting audio devices, chatting with passengers, smoking, eating, and drinking. A structural equation model was constructed to identify the latent variables that have a significant influence on crash risk. The analysis showed that in-vehicle distractions had a high effect on the crash likelihood. The results also indicated that dangerous driving behavior had a direct effect on the crash risk probability, as well as on the rash driving latent variables. The results provide insight into distracted driving behavior among young drivers and can be useful in developing enforcement and educational strategies to reduce this type of behavior.

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Section: Sem Resultsmentioning

confidence: 99%

Investigating in-vehicle distracting activities and crash risks for young drivers using structural equation modeling

Gaweesh

Ahmed

2020

PLoS ONE

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“…where Y i is the natural logarithm of the number of RTAs in i spatial units. However, this study applied two spatial models (SLM and SEM) compared with the OLS model as given in Eq (1). The spatial lag model (SLM) models can be written as…”

Section: Modelsmentioning

confidence: 99%

“…With a view to decrease the road burden, the origin and nature of road traffic accidents (RTA) have been theoretically and empirically investigated by many researchers [1]. Although statistical modelling for RTA has improved significantly in the past years, there are still many gaps to be filled between practical analysis and the frontier analysis [2].…”

Section: Introductionmentioning

confidence: 99%

Comparison of spatial regression models with Road Traffic Accidents Data r

Al-Hasani¹,

Asaduzzaman²,

Soliman³

2019

Proceedings of the International Conference on Statistics: Theory and Applications

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Road traffic accidents (RTA) cause severe problems for the societies in developed and developing countries and result in loss of lives and highly burden cost. Investigators have applied a wide variety of methodological techniques over the years in order to gain more understanding in this discipline. Spatial models, an elegant technique to model spatial data, has been applied to capture localisation effects and influencing factors on RTA. This study aims to compare the spatial lag model (SLM) and the spatial error model (SEM) with the Ordinary least square (OLS) model for the road traffic accident data in the Sultanate of Oman. To compare the performance and accuracy of the OLS, SLM and SEM models, the log-likelihood, Akaike information criterion (AIC), and Bayesian information criteria (BIC) have been used. The SLM model outperforms SEM and OLS models for the data and one of the most significant findings to emerge from this study as the SLM model provides the best values of log-likelihood, AIC and BIC comparing to the values from OLS and SEM models.

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“… 2 Driving violations and mistakes are the two most common risky driving behaviors. 3 Driving violations are associated with the misapplication of normally good rules, the application of bad rules, a failure to apply a good rule, or erroneous performance in a no-rules situation. 4 Driving violations can operationalized into ordinary violations and aggressive violation.…”

Section: Introductionmentioning

confidence: 99%

The Moderating Effects of Emotions on the Relationship Between Self-Reported Individual Traits and Actual Risky Driving Behaviors

Liu

Wang

Guo

2021

PRBM

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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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Measuring risky driving behaviours among young drivers: Development of a scale for the Oman setting

Cited by 23 publications

References 35 publications

Investigating in-vehicle distracting activities and crash risks for young drivers using structural equation modeling

Investigating in-vehicle distracting activities and crash risks for young drivers using structural equation modeling

Comparison of spatial regression models with Road Traffic Accidents Data r

The Moderating Effects of Emotions on the Relationship Between Self-Reported Individual Traits and Actual Risky Driving Behaviors

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