Crash severity modelling using ordinal logistic regression approach

Asare, Isaac Ofori; Mensah, Alice Constance

doi:10.1080/17457300.2020.1790615

Cited by 26 publications

(17 citation statements)

References 35 publications

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“…The significance level was 0.05 which indicates a 5% risk, thus concluding that an association exists, when there is no actual association [55]. If the p-value is less than or equal to the significance level, it is concluded that it is statistically significant between the response variable and the term [57]. Mostly, all the significant ones with p-values are less than 0.05 as shown in Tab.…”

Section: Lr Resultsmentioning

confidence: 96%

Assessing the Risk of University-Student Driving to enhance Safe and Sustainable Traffic Management: A Logistic Regression and Analytic Hierarchy Approach

et al. 2022

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Road accidents have become more common these days and it is pathetic when the accidents happen due to ignorance. The research objective is focused on university students, creating awareness about road safety and improving a sustainable traffic management scenario in the Kingdom of Saudi Arabia. An analytical hierarchy process and a logistic regression model were used to determine the risk priorities ranking of severity factors based on the comparisons of different driver behaviour factors. A cross-sectional survey was conducted among 3200 university students in Saudi Arabia to evaluate the risk associated with accident factors. The main factors taken for risk analysis were Stability during driving, not adhering to rules, committing human errors, Insufficient Visibility, Facing Vehicles Issues. The model estimation analysis revealed the severity, which was based on the student's behavioural factors as a driver which contributed to the high fatality. It is further proposed to teach an interdisciplinary course on Traffic Management to various university students. The awareness towards traffic sense and safety rules would bring down the accidents rates and help the government to maintain smooth traffic density.

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Section: Lr Resultsmentioning

confidence: 96%

Assessing the Risk of University-Student Driving to enhance Safe and Sustainable Traffic Management: A Logistic Regression and Analytic Hierarchy Approach

et al. 2022

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“…Some studies analyze traffic accident patterns [12], risk behaviors [13,14], causes of traffic accidents [9,15], types of traffic collisions [8], and time of occurrence [16]. They also determine that the severity of accidents depends on the vehicles involved [17,18] and demographic characteristics [18][19][20][21][22]. In general, these investigations used historical data on traffic accidents and are descriptive.…”

Section: State Of the Artmentioning

confidence: 99%

Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019

et al. 2021

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Background: Road safety is a significant public health problem because it causes negative consequences on victims and families. The objective was to analyze the most significant changes in traffic crashes in Ecuador during the period from 2000 to 2019. With data obtained from the National Institute of Statistics and Census, we performed the analysis to identify: the number of traffic crashes, the number of victims, and other study variables. Methods: Descriptive and analytical statistics and the contrast of proportions were used to analyze data from 2000 to 2019. Results: According to the ideal joinpoint analysis model, there was a significant decrease in the number of recorded traffic accidents from 2015 to 2019 of −8.54 per year, while the tendency to die increased in females (2.05 per year) and males (3.29 per year). The most common crash was a collision, and the automobile appeared as the most involved vehicle from 2015 to 2019. The hypothesis test contrast is used to determine if statistically significant differences exist between age groups by gender of the driver injured in the period 2017–2018. Conclusions: This study determines the most significant changes in the variables related to traffic crashes, where mortality due to this cause in the last four years has had a growth rate of 1.8% compared to collisions that presented a rate of −31.12%. The contrast of the hypothesis test shows significant differences in the injury level between males and female drivers, depending on the age group.

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“…Many of these RTA related deaths, according to Konlan et al [9], are caused by road traffic behavior of motorcyclists. Predictor variables such as age, alcohol influence, excessive speeding, bad roads, overloading, and disregard for road regulations have been studied to be significant risk factors of road fatalities in Ghana [10][11][12][13]. However, these studies are devoid of vigorous probability models that attempt to predict road traffic human deaths in Ghana.…”

Section: Introductionmentioning

confidence: 99%

Predictive Probability Models of Road Traffic Human Deaths with Demographic Factors in Ghana

2022

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Road traffic carnages are global concerns and seemingly on the rise in Ghana. Several risk factors have been studied as associated with road traffic fatalities. However, inadequate road traffic fatality (RTF) data and inconsistent probability outcomes for RTF remain major challenges. The objective of this study was to illustrate and estimate probability models that can predict road traffic fatalities. We relied on 66,159 recorded casualties who were involved in road traffic accidents (RTAs) in Ghana from 2015 to 2019. Three generalized linear models, namely, logistic regression, probit regression, and linear probability model, were used for the analysis. We found that gender and age groups have significant effects in predicting the probability of road traffic fatality for all three models. Through a likelihood ratio test, however, it was determined that the logit regression model produced consistent probabilities of traffic fatalities which are very close to the actual probability values across the age groups and gender, compared to the other two models. Thus, we recommend intensified campaign for the use of seat belts in vehicles, targeted at the aged and male users of road transport, to reduce the possibility of death in any RTA.

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Crash severity modelling using ordinal logistic regression approach

Cited by 26 publications

References 35 publications

Assessing the Risk of University-Student Driving to enhance Safe and Sustainable Traffic Management: A Logistic Regression and Analytic Hierarchy Approach

Assessing the Risk of University-Student Driving to enhance Safe and Sustainable Traffic Management: A Logistic Regression and Analytic Hierarchy Approach

Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019

Predictive Probability Models of Road Traffic Human Deaths with Demographic Factors in Ghana

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