scholarly journals A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis

2019 ◽  
Vol 11 (2) ◽  
pp. 418 ◽  
Author(s):  
Yajie Zou ◽  
Xinzhi Zhong ◽  
Jinjun Tang ◽  
Xin Ye ◽  
Lingtao Wu ◽  
...  
Keyword(s):  
Regression Model ◽  
Empirical Bayes ◽  
Negative Binomial ◽  
Washington State ◽  
Gaussian Copula ◽  
Copula Model ◽  
Crash Analysis ◽  
Vehicle Collisions ◽  
Vehicle Collision

Wildlife‒vehicle collision (WVC) data usually contain two types: the reported WVC data and carcass removal data. Previous studies often found a discrepancy between the number of reported WVC and carcass removal data, and the quality of both datasets is affected by underreporting. Underreporting means the number of WVCs is not fully recorded in the database; neglecting the underreporting in WVC data may result in biased parameter estimation results. In this study, a copula regression model linking wildlife‒vehicle collisions and the underreporting outcome was proposed to consider the underreporting in WVC data. The WVC data collected from 10 highways in Washington State were analyzed using the copula regression model and the Negative Binomial (NB) model. The main findings from this study are as follows: (1) the Gaussian copula model can provide different modeling results when compared with the conventional modeling approach; (2) the hotspot identification results indicate that the Gaussian copula-based Empirical Bayes (EB) method can more accurately identify hotspots than the NB-based EB method. Thus, the proposed copula model may be a better alternative to the conventional NB model for modeling underreported WVC data.

scholarly journals Comparative Analysis of the Reported Animal-Vehicle Collisions Data and Carcass Removal Data for Hotspot Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Xiaoxue Yang ◽  
Yajie Zou ◽  
Lingtao Wu ◽  
Xinzhi Zhong ◽  
Yinhai Wang ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Negative Binomial ◽  
Washington State ◽  
Empirical Bayesian ◽  
Transportation Management ◽  
Vehicle Collisions ◽  
Explanatory Variables ◽  
Vehicle Collision ◽  
Different Characteristics ◽  
Better Than

Two common types of animal-vehicle collision data (reported animal-vehicle collision (AVC) data and carcass removal data) are usually recorded by transportation management agencies. Previous studies have found that these two datasets often demonstrate different characteristics. To accurately identify the higher-risk animal-vehicle collision sites, this study compared the differences in hotspot identification and the effect of explanation variables between carcass removal and reported AVCs. To complete the objective, both the Negative Binomial (NB) model and the generalized Negative Binomial (GNB) are applied in calculating the Empirical Bayesian (EB) estimates using the animal collision data collected on ten highways in Washington State. The important findings can be summarized as follows. (1) The explanatory variables have different effects on the occurrence of carcass removal data and reported AVC data. (2) The ranking results from EB estimates when using carcass removal data and reported AVC data differ significantly. (3) The results of hotspot identification are different between carcass removal data and reported AVC data. However, the ranking results of GNB models are better than those of NB models in terms of consistency. Thus, transportation management agencies should be cautious when using either carcass removal data or reported AVC data to identify hotspots.

Case Exercise: Biomechanics in Rearend Motor Vehicle Collisions

2007 ◽  
Vol 12 (3) ◽  
pp. 4-7
Author(s):  
Charles N. Brooks ◽  
Christopher R. Brigham
Keyword(s):  
Motor Vehicle ◽  
Body Position ◽  
Motor Vehicle Collisions ◽  
Time Curve ◽  
Vehicle Collisions ◽  
Need For Treatment ◽  
Multiple Factors ◽  
Bodily Injury ◽  
Vehicle Collision ◽  
Physical Findings

Abstract Multiple factors determine the likelihood, type, and severity of bodily injury following a motor vehicle collision and, in turn, influence the need for treatment, extent of disability, and likelihood of permanent impairment. Among the most important factors is the change in velocity due to an impact (Δv). Other factors include the individual's strength and elasticity, body position at the time of impact, awareness of the impending impact (ie, opportunity to brace, guard, or contract muscles before an impact), and effects of braking. Because Δv is the area under the acceleration vs time curve, it combines force and duration and is a useful way to quantify impact severity. The article includes a table showing the results of a literature review that concluded, “the consensus of human subject research conducted to date is that a single exposure to a rear-end impact with a Δv of 5 mph or less is unlikely to result in injury” in most healthy, restrained occupants. Because velocity incorporates direction as well as speed, a vehicular occupant is less likely to be injured in a rear impact than when struck from the side. Evaluators must consider multiple factors, including the occupant's pre-existing physical and psychosocial status, the mechanism and magnitude of the collision, and a variety of biomechanical variables. Recommendations based solely on patient history and physical findings (and, perhaps, imaging studies) may be ill-informed.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Study on Application of Oat Flour in Quick-Frozen Dumpling Production

2013 ◽  
Vol 634-638 ◽  
pp. 1532-1536
Author(s):  
Chang Xin Ji ◽  
Xiao Yan Jing ◽  
Yan Qi Liu
Keyword(s):  
Regression Model ◽  
Correlation Coefficient ◽  
Sensory Evaluation ◽  
Outward Appearance ◽  
Optimal Amount ◽  
Oat Flour ◽  
Model Equations

Quick-frozen oat dumplings were produced by adding some Oat flour in dumpling wrapper. The effect of oat flour ratio on the optimal amount of water of the bland flour, cracking ratio and edible quality of quick-frozen oat dumpling were researched. The optimal amount of water of per gram Xiang Man Yuan dumpling flour and oat flour are 0.43g and 0.63g individually. The cracking ratio of products increases with the increases of oat flour ratio. The effect of oat flour ratio on the sensory evaluation and the outward appearance score of quick-frozen oat dumplings is very significant in 1‰ level, their correlation coefficient are 0.990 and 0.994 respectively, and the regression model equations are as follows: Y=90.094－112.477X; Y=18.519－21.763X.

scholarly journals Analysis of Vehicle Collision on an Assembled Anti-Collision Guardrail

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5152
Author(s):  
Juncheng Yao ◽  
Bo Wang ◽  
Yujie Hou ◽  
Liang Huang
Keyword(s):  
Working Conditions ◽  
Traffic Accidents ◽  
Analysis Software ◽  
Time Curve ◽  
Vehicle Collisions ◽  
Moving Vehicle ◽  
Vehicle Collision ◽  
New Type ◽  
Main Girder ◽  
Protective Performance

Traffic accidents such as vehicle collisions with bridge guardrails occur frequently. These accidents cause damage to the driver and the vehicle as well as the bridge. A new type of assembled anti-collision guardrail is proposed in this study. LS-DYNA is a nonlinear display dynamic analysis software used to evaluate the safety of a new type of assembled anti-collision guardrail. A specific, numerically analyzed model of vehicle–guardrail collision is established using LS-DYNA. The energy distribution–time curve of the vehicle collision process is obtained. After comparison with measured data from the vehicle collision test, the model of vehicle–guardrail collision is verified as being correct. Based on this, we analyze the process of a vehicle collision on the assembled anti-collision guardrail. The result shows that the assembled anti-collision guardrail proposed in this paper can better change the trajectory of a moving vehicle and can prevent the vehicle from falling off the bridge. From the car body collision results, the assembled anti-collision guardrail for bridges proposed in this paper can reduce vehicle damage and can protect the driver effectively. From the analysis of the main girder stress on the bridge, an anti-collision guardrail installed on an existing bridge will not cause damage to the main girder during a collision. In order to study the influence of the four parameters on the anti-collision effect, we carried out a comparative calculation of multiple working conditions. The results show that the new type of assembled anti-collision guardrail has good protective performance under different working conditions.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

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