scholarly journals Analysis of Traffic Crash Severity on Freeway Using Hierarchical Binomial Logistic Model

2011 ◽  
Vol 13 (4) ◽  
pp. 199-209 ◽  
Author(s):  
Sung-Ra Mun ◽  
Young-Ihn Lee
2018 ◽  
Vol 15 (5) ◽  
pp. 27-39
Author(s):  
E. V. Ermakova

Study purpose. The paper shows the application of statistical methods for the trade credit management in the wholesale Russian companies. In this industry, the companies deal with a huge amount of customers, while trade credit is a common practice. As a result, fast and reasonable choice of trade credit terms becomes especially important for wholesale companies. The main study purpose is to provide the methods to choose the trade credit terms.Materials and methods. In this paper, the methods for trade credit management are based of the empirical research where binomial logistic model and discriminant analysis were used. The binomial logistic model was used to assess the customers’ reliability, his inclination to violate the terms specified in the contract. The delay period must be chosen when trade credit is provided. In the paper, the discriminant analysis was applied to make the decision. The discriminant functions allow choosing such a period of delay that will be broken with the least probability by the customer with certain financial and non-financial characteristics. The data used refer to 11 Russian companies from the wholesale industry and include 720 observations for 2016-2017.Results. As a result, the possibility of due repayment may be evaluated and the payment delay may be selected according to individual customers’ characteristics. Eight factors that characterize the liquidity of the purchaser, its profitability, turnover, and non-financial factors became significant to assess the reliability. In conclusion, the paper contains the practical example for four hypothetical purchasers with different characteristics. The higher the reliability of the customer, the more attractive conditions can be offered for him, depending on the propensity to risk of the wholesale company, as well as its financial opportunities.Conclusion. This article contains the model to evaluate the possibility of due repayment and algorithm to select the payment delay, which are based on the binomial logistic model and classification functions. Although there are a large number of methods to select the terms of trade credit, the majority of them have serious limitations. The most of methods are based only on the professional experience, while statistical analysis, in presence, is based on data of one company because of the confidentiality of necessary information. In contrast, this article is based on the empirical data and includes the delay period selection, which is slightly enlightened in the literature.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 63288-63302 ◽  
Author(s):  
Fang Zong ◽  
Xiangru Chen ◽  
Jinjun Tang ◽  
Ping Yu ◽  
Ting Wu

Author(s):  
Khaled Assi

The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal component analysis (PCA) with multilayer perceptron neural networks (MLP-NN) and support vector machines (SVM) in predicting RTC severity. PCA shows that the first nine components have an eigenvalue greater than one. The cumulative variance percentage explained by these principal components was found to be 67%. The prediction accuracies of the models developed using the original attributes were compared with those of the models developed using principal components. It was found that the testing accuracies of MLP-NN and SVM increased from 64.50% and 62.70% to 82.70% and 80.70%, respectively, after using principal components. The proposed models would be beneficial to trauma centers in predicting crash severity with high accuracy so that they would be able to prepare for appropriate and prompt medical treatment.


2021 ◽  
Vol 32 (4) ◽  
pp. 15-28
Author(s):  
Guanlong Li ◽  
Yueqing Li ◽  
Yalong Li ◽  
Brian Craig ◽  
Xing Wu

Driving is the essential means of travel in Southeast Texas, a highly urbanized and populous area that serves as an economic powerhouse of the whole state. However, driving in Southeast Texas is subject to many risks as this region features a typical humid subtropical climate with long hot summers and short mild winters. Local drivers would encounter intense precipitation, heavy fog, strong sunlight, standing water, slick road surface, and even frequent extreme weather such as tropical storms, hurricanes and flood during their year-around travels. Meanwhile, research has revealed that the fatality rate per 100 million vehicle miles driven in urban Texas became considerably higher than national average since 2010, and no conclusive study has elucidated the association between Southeast Texas crash severity and potential contributing factors. This study used multiple correspondence analysis (MCA) to examine a group of contributing factors on how their combinatorial influences determine crash severity by creating combination clouds on a factor map. Results revealed numerous significant combinatorial effects. For example, driving in rain and extreme weather on a wet road surface has a higher chance in causing crashes that incur severe or deadly injuries. Besides, other contributing factors involving risky behavioral factors, road designs, and vehicle factors were well discussed. The research outcomes could inspire local traffic administration to take more effective countermeasures to systematically mitigate road crash severity.


2015 ◽  
Vol 141 (10) ◽  
pp. 04015020 ◽  
Author(s):  
Jaeyoung Lee ◽  
BooHyun Nam ◽  
Mohamed Abdel-Aty

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