A matter of style? Testing the moderating effect of driving styles on the relationship between job strain and work-related crashes of professional drivers

Useche, Sergio A.; Cendales, Boris; Alonso, Francisco; Orozco-Fontalvo, Maurício

doi:10.1016/j.trf.2020.05.015

Cited by 25 publications

(19 citation statements)

References 52 publications

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“…The CFA results were evaluated using the following statistics: Chi-square ( X 2 ), minimum discrepancy ratio ( X df 2 / ), comparative form index ( CFI ), normed fit index ( NFI ), Tucker-Lewis index ( TLI ), incremental fit index ( IFI ), goodness-of-fit index ( GFI ), adjusted goodness-of-fit index ( AGFI ) and root mean square error of approximation ( RMSEA ). According to the CFA literature, especially the works of Marsh et al (2004), Hooper et al (2008, and Useche et al (2020), the recommended thresholds for each of the statistics were defined in Table 2. When the CFA did meet the recommended threshold, especially the RMSEA, we proceeded to assess which variables had standardized regression values of less than 0.6 for elimination, as well as the modifications in the covariances of the errors of each of the factors from the largest and theoretically most parsimonious modification indices as outlined by Marsh et al (2004).…”

Section: Cfamentioning

confidence: 99%

Student experience scale: Factor analysis in higher education institutions

Mateus

Rincón²,

Acosta

2023

Front. Educ.

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IntroductionIn the context of higher education, the student experience (SX) has an important impact on student satisfaction and the perceived value of the training service received. For these reasons, higher education institutions (HEIs), which are focused on student-centred learning, need to know more about the experience that results from student interaction with the service and support systems that meet their needs in these institutions. However, despite the progress made in the analysis of SX, further research is still needed to know how to measure and manage it, and for this, it is essential to know the student’s perceptions.MethodsFor this reason, an SX scale was selected to have a suitable tool for experience management. Thus, the psychometric properties of the selected scale were evaluated in undergraduate students at two Colombian universities. The validation process consisted of an adaptation of the scale, a content validity analysis, an exploratory factor analysis and a confirmatory factor analysis.ResultsThe results were satisfactory showing the reliability and validity of the student experience scale in the context of universities.DiscussionTherefore, its cross-cultural goodness is confirmed in the student segment and it is suggested to perform this same process for other university audiences.

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Section: Cfamentioning

confidence: 99%

Student experience scale: Factor analysis in higher education institutions

Mateus

Rincón²,

Acosta

2023

Front. Educ.

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“…The recurrent unit structure of LSTM is shown in Fig 2 . The calculation process is: firstly calculate the three gates and ct through x t and h t-1 , namely formula (4) to formula (7); then combine f t and e t to update c t , namely formula (2); finally, combine o t passes information to h t , which is formula (3).…”

Section: Memory Module (Mm)mentioning

confidence: 99%

“…Simulation of the driver's driving behavior is the most direct way to forecast for distracted driving behavior, however, a different driver's driving skills, driving style, emergency ability, mood swings, mental status, education background, life experience, each is not identical [3][4][5][6][7], such as environment such as road conditions, weather, illumination, time of day also make a big difference [8], These uncertain factors make it difficult to simulate individual driving behavior objectively. However, in the process of driving, no matter what factors the vehicle and the driver is affected by, the distracted driving behavior will eventually be reflected by the vehicle and the driver's behavior.…”

Section: Introductionmentioning

confidence: 99%

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

Meng

Wang

et al. 2022

PLoS ONE

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Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensors installed on the vehicle can be identified by the algorithm to obtain the distracted driving behavior data, which can be used to predict the driving behavior risk of the vehicle and the area. In this paper, a new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data. The network consists of three modules: the Feature Processing Module, the Memory Module, and the Prediction Module. In this process, attribute data (time in a day, daily driving time, and daily driving mileage) that can reflect external factors and driver statuses, are added to the network to increase the accuracy of the model. We predicted the driving behavior risk of different objects (Vehicle and Area). For the applicability improvement of the model, we further classify the distracted driving behavior categories, and DBRPNN can provide more accurate risk prediction. The results show that compared with traditional models (Classification and Regression Tree, Support Vector Machines, Recurrent Neural Network, and Long Short-Term Memory), DBRPNN has better prediction performance. The method proposed in this paper has been fully verified and may be transplanted into active safety early warning system for more accurate and flexible application.

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“…Case by case modeling driver’s personal driving behavior is the most straightforward way; however, dangerous driving behavior involves various complex and uncertain factors, such as driving skills, emergency response ability, gender, mood, fatigue, job pressure and even educational background, life experience, etc. ( Horswill and McKenna, 1999 ; Harre and Sibley, 2007 ; Dula et al, 2011 ; Day et al, 2018 ; Fountas et al, 2019 ; Useche et al, 2020 ), thereby making it difficult to directly study personal driving behavior. Nevertheless, during the course of driving, no matter how complex factors the vehicle is subjected to and no matter what driving actions the driver takes, all dangerous driving behaviors will eventually be reflected through the corresponding motion state of vehicle and reaction of passengers on the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network

et al. 2021

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Dangerous driving behavior is the leading factor of road traffic accidents; therefore, how to predict dangerous driving behavior quickly, accurately, and robustly has been an active research topic of traffic safety management in the past decades. Previous works are focused on learning the driving characteristic of drivers or depended on different sensors to estimate vehicle state. In this paper, we propose a new method for dangerous driving behavior prediction by using a hybrid model consisting of cloud model and Elman neural network (CM-ENN) based on vehicle motion state estimation and passenger’s subjective feeling scores, which is more intuitive in perceiving potential dangerous driving behaviors. To verify the effectiveness of the proposed method, we have developed a data acquisition system of driving motion states and apply it to real traffic scenarios in ShenZhen city of China. Experimental results demonstrate that the new method is more accurate and robust than classical methods based on common neural network.

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A matter of style? Testing the moderating effect of driving styles on the relationship between job strain and work-related crashes of professional drivers

Cited by 25 publications

References 52 publications

Student experience scale: Factor analysis in higher education institutions

Student experience scale: Factor analysis in higher education institutions

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network

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