Accident Prediction Models With and Without Trend: Application of the Generalized Estimating Equations Procedure

2000 ◽  
Vol 1717 (1) ◽  
pp. 102-108 ◽  
Author(s):  
Dominique Lord ◽  
Bhagwant N. Persaud
Keyword(s):  
Generalized Estimating Equations ◽  
Prediction Models ◽  
Estimating Equations ◽  
Temporal Correlation ◽  
Expected Number ◽  
Accident Prediction ◽  
Site Characteristics ◽  
Accident Data ◽  
A Site ◽  
Generalized Estimating

Accident prediction models (APMs) are useful tools for estimating the expected number of accidents on entities such as intersections and road sections. These estimates typically are used in the identification of sites for possible safety treatment and in the evaluation of such treatments. An APM is, in essence, a mathematical equation that expresses the average accident frequency of a site as a function of traffic flow and other site characteristics. The reliability of an APM estimate is enhanced if the APM is based on data for as many years as possible, especially if data for those same years are used in the safety analysis of a site. With many years of data, however, it is necessary to account for the year-to-year variation, or trend, in accident counts because of the influence of factors that change every year. To capture this variation, the count for each year is treated as a separate observation. Unfortunately, the disaggregation of the data in this manner creates a temporal correlation that presents difficulties for traditional model calibration procedures. An application is presented of a generalized estimating equations (GEE) procedure to develop an APM that incorporates trend in accident data. Data for the application pertain to a sample of four-legged signalized intersections in Toronto, Canada, for the years 1990 through 1995. The GEE model incorporating the time trend is shown to be superior to models that do not accommodate trend and/or the temporal correlation in accident data.

Predicting Distal Radius Bone Strains and Injury in Response to Impacts Using Multi-Axial Accelerometers

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Timothy A. Burkhart ◽  
Cynthia E. Dunning ◽  
David M. Andrews
Keyword(s):  
Generalized Estimating Equations ◽  
Prediction Models ◽  
Logistic Models ◽  
Estimating Equations ◽  
Strain Gauges ◽  
Injury Prediction ◽  
Bone Injury ◽  
Axial Acceleration ◽  
General Response ◽  
Generalized Estimating

Measuring a bone’s response to impact has traditionally been done using strain gauges that are attached directly to the bone. Accelerometers have also been used for this purpose because they are reusable, inexpensive and can be attached easily. However, little data are available relating measured accelerations to bone injury, or to judge if accelerometers are reasonable surrogates for strain gauges in terms of their capacity to predict bone injuries. Impacts were applied with a custom designed pneumatic impact system to eight fresh-frozen human cadaveric radius specimens. Impacts were repeatedly applied with increasing energy until ultimate failure occurred. Three multiaxial strain gauge rosettes were glued to the bone (two distally and one proximally). Two multiaxial accelerometers were attached to the distal dorsal and proximal volar aspects of the radius. Overall, peak minimum and maximum principal strains were calculated from the strain-time curves from each gauge. Peak accelerations and acceleration rates were measured parallel (axial) and perpendicular (off-axis) to the long axis of the radius. Logistic generalized estimating equations were used to create strain and acceleration-based injury prediction models. To develop strain prediction models based on the acceleration variables, Linear generalized estimating equations were employed. The logistic models were assessed according to the quasi-likelihood under independence model criterion (QIC), while the linear models were assessed by the QIC and the marginal R2. Peak axial and off-axis accelerations increased significantly (with increasing impact energy) across all impact trials. The best injury prediction model (QIC = 9.42) included distal resultant acceleration (p < 0.001) and donor body mass index (BMI) (p < 0.001). Compressive and tensile strains were best predicted by separate uni-variate models, including peak distal axial acceleration (R2 = 0.79) and peak off-axis acceleration (R2 = 0.79), respectively. Accelerometers appear to be a valid surrogate to strain gauges for measuring the general response of the bone to impact and predicting the probability of bone injury.

scholarly journals Fatores socioeconômicos que influenciam na disposição de consumidores em adquirir carne bovina com certificação de origem em Belo Horizonte, Minas Gerais, Brasil

2016 ◽  
Vol 83 (0) ◽  
Author(s):  
Danielle Rodrigues Magalhães ◽  
Marcos Aurélio Lopes ◽  
Christiane Maria Barcellos Magalhães da Rocha ◽  
Fábio Raphael Pascoti Bruhn ◽  
Jerry Carvalho Borges ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Minas Gerais ◽  
Belo Horizonte ◽  
Generalized Estimating

RESUMO: Objetivou-se verificar a influência dos fatores socioeconômicos na disposição de 407 consumidores em hipermercados do município de Belo Horizonte, Minas Gerais, em adquirir carne bovina com certificação de origem e verificar as características inerentes ao produto que auxiliam o consumidor no momento da compra, considerando o seu conhecimento prévio sobre rastreabilidade e certificação de origem. Foi realizada uma análise descritiva de todas as variáveis e, posteriormente, realizada a análise univariada pelo teste do qui-quadrado ou exato de Fischer. As variáveis foram adicionadas no modelo múltiplo da regressão logística Generalized Estimating Equations (GEE) e para todas as variáveis presentes no modelo final (p ≤ 0,05) foi calculado o risco por meio da odds ratio (OR) ajustada a um intervalo de confiança de 95%. Todos os fatores socioeconômicos analisados (sexo, idade, renda e escolaridade) influenciaram na disposição de consumidores em adquirir carne bovina com certificação de origem. Os atributos intrínsecos mais importantes na tomada de decisão no momento da compra da carne foram cor, maciez, odor e a pouca quantidade de gordura; enquanto que os atributos extrínsecos foram preço, selo de qualidade e carimbo do SIF. A maioria dos consumidores conhece o conceito correto de rastreabilidade e acreditam que o maior benefício da carne rastreada é oferecer mais segurança e evitar riscos de doenças transmitidas pelos alimentos; e a desvantagem é ser um produto mais caro do que o convencional.

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