Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

Hosseinzadeh, Aryan; Moeinaddini, Amin; Ghasemzadeh, Ali

doi:10.1016/j.jsr.2021.02.012

Cited by 82 publications

(44 citation statements)

References 57 publications

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“…Hypertension is the most common comorbidity among Covid-19 patients and it has been frequently reported that hypertension can increase the severity of Covid-19 infection. Thus, many people with hypertension are worried that they will end up with severe Covid-19 infection and poor clinical outcomes (21).…”

Section: The Effect Of Hypertension On Deaths Due To Covid-19mentioning

confidence: 99%

Risk Factors of Death Due to Corona Virus Disease 19 in Kendari City

Karmila¹,

Supodo²,

Sunarsih³

2021

ijhsrd

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Background : Corona virus Disease 2019 or Covid-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Corona virus 2 (SARS-Cov-2). Data from the Kendari City Health Office from January to December 2020, the number of confirmed cases of Covid-19 was 3,874 cases with 56 deaths spread throughout Kendari City. The high death rate from corona virus disease19 is caused by several factors. The research objective was to determine the risk factors for death due to corona virus disease 19 (covid-19) in Kendari City. Methods: This type of research is a case control study conducted from March to April 2021, the number of sample are 49 cases taken by random sampling and 49 controls. Results: Based on the Odds Ratio (OR) value of pneumonia disease is 8,624> 1, OR of Diabetes Mellitus is 7,994> 1, and the respondent's OR of hypertension is 8,653> 1. Conclusion: Pneumonia, diabetes mellitus, and hypertension are the risk factors of death from Covid-19 in Kendari City in 2020. A person suffering from hypertension has the greatest risk of death. Furthermore, the second greatest risk for death is in someone who has pneumonia.

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Section: The Effect Of Hypertension On Deaths Due To Covid-19mentioning

confidence: 99%

Risk Factors of Death Due to Corona Virus Disease 19 in Kendari City

Karmila¹,

Supodo²,

Sunarsih³

2021

ijhsrd

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“…Expressway freight transportation can promote rapid development of the economy ( 2 – 4 ). Expressway traffic accidents are usually very serious due to the high speed of vehicles ( 5 ), and the heavy weight and large size of trucks ( 6 – 9 ). Therefore, greater attention should be paid to ensuring truck driving safety on expressways.…”

Section: Introductionmentioning

confidence: 99%

Safety-oriented planning of expressway truck service areas based on driver demand

Ding

Wang

Chu

et al. 2022

Front. Public Health

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The rapid development of the economy has promoted the growth of freight transportation. The truck service areas on expressways, as the main places for truck drivers to rest, play an important role in ensuring the driving safety of trucks. If these service areas are constructed densely or provide a plentiful supply of parking areas, they are costly to construct. However, if the distance between two adjacent truck service areas is very large or the number of truck parking spaces in service areas is small, the supply will fail to meet the parking needs of truck drivers. In this situation, the continuous working time of truck drivers will be longer, and this is likely to cause driver fatigue and even traffic accidents. To address these issues, this paper established a non-linear optimization model for truck service area planning of expressways to optimize truck driving safety. An improved genetic algorithm is proposed to solve the model. A case study of a 215.5-kilometers-length section of the Guang-Kun expressway in China was used to demonstrate the effectiveness of the model and algorithm. As validated by this specific case, the proposed model and solution algorithm can provide an optimal plan for the layout of truck service areas that meet the parking needs of truck drivers while minimizing the service loss rate. The research results of this paper can contribute to the construction of truck service areas and the parking management of trucks on expressways.

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“…There are studies to estimate the relationship between the level of injury caused by an accident and the severity of the injury from a medical aspect. In these cases, probability models have been mainly used for traffic accident research, and logistic regression has been used in binary studies, especially mortality [14,15]. Wei and Chiu (2002) used decision trees to perform their analysis, while Coussement and Van den Poel (2008) used and compared techniques such as support vector machines, random forests, and logistic regression [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…In order to overcome the limitations of statistical models, a non-probabilistic data mining approach has been widely used recently, and among them, clustering [21,22], classification, and regression tree methods [23][24][25] have been adopted. In addition, among these non-parametric models, SVM was developed as a method to improve regression and classification problems and is used in the study on the severity of injuries in traffic accidents [14,26]. Especially using SVMs, studies were focused on investigating methods to increase its accuracy using radial basis function (RBF) [27].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Prediction Models for Mortality Related to Injuries from Road Traffic Accidents after Correcting for Undersampling

Boo

Choi

2021

IJERPH

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In this study, four models—logistic regression (LR), random forest (RF), linear support vector machine (SVM), and radial basis function (RBF)-SVM—were compared for their accuracy in determining mortality caused by road traffic injuries. They were tested using five years of national-level data from the Korea Disease Control and Prevention Agency’s (KDCA) National Hospital Discharge In-Depth Survey (2013 through to 2017). Model performance was measured for accuracy, precision, recall, F1 score, and Brier score metrics using classification analysis that included characteristics of patients, accidents, injuries, and illnesses. Due to the number of variables and differing units, the rates of survival and mortality related to road traffic accidents were imbalanced, so the data was corrected and standardized before the classification models’ performances were compared. Using the importance analysis, the main diagnosis, the type of injury, the site of the injury, the type of injury, the operation status, the type of accident, the role at the time of the accident, and the sex were selected as the analysis factors. The biggest contributing factor was the role in the accident, which is the driver, and the major sites of the injuries were head injuries and deep injuries. Using selected factors, comparisons of the classification performance of each model indicated RBF-SVM and RF models were superior to the others. Of the SVM models, the RBF kernel model was superior to the linear kernel model; it can be inferred that the performance of the high-dimensional transformed RBF model is superior when the dimension is complex because of the use of multiple variables. The findings suggest there are limitations to analyses involving imbalanced, multidimensional original data, such as data on road traffic mortality. Thus, analyses must be performed after imbalances are corrected.

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Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

Cited by 82 publications

References 57 publications

Risk Factors of Death Due to Corona Virus Disease 19 in Kendari City

Risk Factors of Death Due to Corona Virus Disease 19 in Kendari City

Safety-oriented planning of expressway truck service areas based on driver demand

Comparison of Prediction Models for Mortality Related to Injuries from Road Traffic Accidents after Correcting for Undersampling

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