Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics...

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Published inSustainability Vol. 11; no. 11; p. 3169
Main Authors Park, Ho-Chul, Joo, Yang-Jun, Kho, Seung-Young, Kim, Dong-Kyu, Park, Byung-Jung
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2019
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Abstract Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.
AbstractList Because cross-group heterogeneity is neglected in hierarchical data, a two-level model can underestimate the standard error, thereby resulting in a type I statistical error when testing statistical hypotheses [13]. [...]if this assumption is inappropriate for a given set of data, the model may produce biased results, underestimated standard errors, and indicate confidence intervals that are too narrow. [...]when there is a correlation between the factors that affect individual crashes and the upper group of the factors, multi-level modeling is suitable because it prevents inaccurate or biased estimates caused by ignoring the hierarchical structure of the crash data. [...]the models can be difficult to apply because of the complexity of estimation. [...]if the non-normality of errors (residuals) cannot be easily solved by, for example, by transforming the dependent variable, it is worth exploring other approaches, such as non-parametric estimation of random effects. [...]improvements in crash-level factors, such as speeding, can be considered as a priority, while the effect of company-level and regional-level factors (e.g., socioeconomic and demographic factors) should be considered from the long-term perspective.
Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.
Author Kho, Seung-Young
Park, Ho-Chul
Joo, Yang-Jun
Park, Byung-Jung
Kim, Dong-Kyu
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Snippet Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the...
Because cross-group heterogeneity is neglected in hierarchical data, a two-level model can underestimate the standard error, thereby resulting in a type I...
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SubjectTerms Buses
Company structure
Confidence intervals
Demographics
Dependent variables
Fatalities
Heterogeneity
Injuries
Normality
Parameter estimation
Pedestrians
Socioeconomic factors
Standard error
Statistical analysis
Statistics
Structural hierarchy
Sustainability
Traffic accidents & safety
Title Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors
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