Seat Belt Injuries

2019 ◽  
Author(s):  
Team DFTB
Keyword(s):  
Seat Belt

Estimation of Driver's Posture Using Body Pressure Level from Seat Belt

2010 ◽  
Vol 130 (12) ◽  
pp. 2118-2123
Author(s):  
Akihiro Imai ◽  
Naoki Tokitsu ◽  
Koji Oguri
Keyword(s):  
Seat Belt ◽  
Pressure Level

Seat-belt injury of the abdominal aorta.

1993 ◽  
Vol 160 (3) ◽  
pp. 661-662 ◽  
Author(s):  
A C Moshyedi ◽  
J Baxter
Keyword(s):  
Abdominal Aorta ◽  
Seat Belt ◽  
Seat Belt Injury

scholarly journals Clinical Significance of the Seat Belt Sign as Evidence of a Compromised Occupant–Seat Belt Relationship

2019 ◽  
Vol 56 (6) ◽  
pp. 624-632
Author(s):  
Mathew Greenston ◽  
Rawson L. Wood ◽  
Lars Reinhart
Keyword(s):  
Clinical Significance ◽  
Seat Belt

scholarly journals Accommodating Taste and Scale Heterogeneity for Front-Seat Passenger’ Choice of Seat Belt Usage

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 460 ◽  
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Mixed Model ◽  
Seat Belt ◽  
Model Fit ◽  
Model Parameters ◽  
Extended Model ◽  
Random Parameters ◽  
Front Seat ◽  
Scale Heterogeneity ◽  
Error Terms ◽  
The Cost

There is growing interest in implementation of the mixed model to account for heterogeneity across population observations. However, it has been argued that the assumption of independent and identically distributed (i.i.d) error terms might not be realistic, and for some observations the scale of the error is greater than others. Consequently, that might result in the error terms’ scale to be varied across those observations. As the standard mixed model could not account for the aforementioned attribute of the observations, extended model, allowing for scale heterogeneity, has been proposed to relax the equal error terms across observations. Thus, in this study we extended the mixed model to the model with heterogeneity in scale, or generalized multinomial logit model (GMNL), to see if accounting for the scale heterogeneity, by adding more flexibility to the distribution, would result in an improvement in the model fit. The study used the choice data related to wearing seat belt across front-seat passengers in Wyoming, with all attributes being individual-specific. The results highlighted that although the effect of the scale parameter was significant, the scale effect was trivial, and accounting for the effect at the cost of added parameters would result in a loss of model fit compared with the standard mixed model. Besides considering the standard mixed and the GMNL, the models with correlated random parameters were considered. The results highlighted that despite having significant correlation across the majority of the random parameters, the goodness of fits favors more parsimonious models with no correlation. The results of this study are specific to the dataset used in this study, and due to the possible fact that the heterogeneity in observations related to the front-seat passengers seat belt use might not be extreme, and do not require extra layer to account for the scale heterogeneity, or accounting for the scale heterogeneity at the cost of added parameters might not be required. Extensive discussion has been made in the content of this paper about the model parameters’ estimations and the mathematical formulation of the methods.

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