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

Joo, Young Chang; Kho, Seung-Young; Kim, Dong Kyu; Park, Byung-Jung

doi:10.3390/su11113169

Cited by 17 publications

(6 citation statements)

References 43 publications

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“…The findings of this study are in line with those of Onieva-García et al [63], Toran Pour et al [64], Park and Ko [65], Li and Fan [44], and Kim et al [66], who confirmed the significant roles of age and gender in pedestrian-related crashes and deaths. Several studies also confirmed the effects of bus involvement on the risks of injury and death of pedestrians (e.g., [67][68][69]), which indicates the significant role of crash attributes in the prediction of PDRC. Overall, when personal characteristics and crash features are factored in, this model appears to be successful and accurate.…”

Section: Variable Significancementioning

confidence: 75%

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

et al. 2022

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More than 8000 pedestrians were killed due to road crashes in Australia over the last 30 years. Pedestrians are assumed to be the most vulnerable users of roads. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. It is critical to know the causes of pedestrian injuries in order to enhance the safety of these vulnerable road users. To achieve this, traditional statistical models are used frequently. However, they have been criticized for their inflexibility in handling outliers and missing or noisy data, and their strict pre-assumptions. This study applied an advanced machine learning algorithm, a Bayesian neural network, which has the characters of both Bayesian theory and neural networks. Several structures of this model were built, and the best structure was selected, which included three hidden neuron layers—sixteen hidden nodes in the first layer and eight hidden nodes in the second and third layers. The performance of this model was compared with the performances of some other machine learning techniques, including standard Bayesian networks, a standard neural network, and a random forest model. The Bayesian neural network model outperformed the other models. In addition, a study on the importance of the features showed that the individuals’ characteristics, time, and circumstantial factors were essential. They greatly increased model performance if the model used them. This research lays the groundwork for using machine learning approaches to alleviate pedestrian deaths caused by road accidents.

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Section: Variable Significancementioning

confidence: 75%

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

et al. 2022

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“…In addition, factors such as drinking status [38], driving experience [32], time [39], road structure [40], vehicle age [41], and visibility [42] have been considered as individual characteristics of pedestrian crashes.…”

Section: Individual Characteristicsmentioning

confidence: 99%

Investigating the Factors Influencing Pedestrian–Vehicle Crashes by Age Group in Seoul, South Korea: A Hierarchical Model

Park

2020

Sustainability

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Walking is the most natural form of moving in everyday life. However, the urban environment is not always safe for walking. Hence, it is appropriate to create pedestrian safety-oriented neighborhood environments to promote walkability. This study identifies factors that can affect the severity of pedestrian injuries by considering and comparing the individual characteristics of pedestrian crashes and the built environment of neighborhoods using a hierarchical model. The main results of this study are as follows. Those under 18 and over 65 years of age, which are recognized as vulnerable road users, are more affected by the neighborhood environments than other age groups. Parks, convenience stores, and restaurants are likely to play a significant role in reducing the severity of pedestrian injuries. However, for pedestrians under 18 years of age, convenience stores that students often visit after school are likely to increase the severity of pedestrian injuries. The severity of pedestrian injuries is likely to be lower at non-signalized crosswalks and intersections than at signalized crosswalks and intersections. The study is expected to contribute to existing literature on the topic and guide policy makers and planners to arrive at consensus approaches when making decisions on how to build pedestrian safety-oriented neighborhoods.

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“…Driver’s residence, working hours, income satisfaction, and negative passenger behavior were identified as major contributing factors based on an ordered probit model ( 20 ). Park et al analyzed factors influencing bus-pedestrian traffic accidents ( 21 ). The identified regional-level factors included the proportion of elderly people, transport infrastructure budget, and the number of medical doctors.…”

mentioning

confidence: 99%

“…The identified regional-level factors included the proportion of elderly people, transport infrastructure budget, and the number of medical doctors. Also, they suggested safety countermeasures based on the derived factors ( 21 ).…”

mentioning

confidence: 99%

Analysis of Intrinsic Factors Leading to Aggressive Driving Behavior to Derive Safety Policy Implications for Bus Drivers

Cho

Park

2022

Transportation Research Record: Journal of the Transportation R

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Significant efforts to develop effective safety policies are essential to prevent bus traffic accidents because city buses accommodate many passengers. An in-depth analysis of causal factors that can be the intrinsic cause of aggressive driving is a first step toward preparing for effective bus safety policies. This study aims to explore traffic safety policy implications and analyze development priorities based on the aforementioned factors including the working environment, health characteristics, human characteristics, living environment, and bus route characteristics. Bus driver survey data obtained by self-reported survey instruments were used to discover factors that affect aggressive driving behavior captured by an in-vehicle data recorder. A binary logistic regression model was established using driver characteristics as independent variables and the level of aggressive driving behavior as the dependent variables. From the model, a total of nine independent variables that have a statistically significant effect on aggressive driving behavior by bus drivers were found. This study then suggested seven bus safety policy implications based on the identified influencing factors. The results of the prioritizing the policy implications show that detailed regulations related to break times for city bus drivers, improvement of company break rooms, and management measures for bus driver mental health and psychological conditions were the top three. The findings of this study would be valuable for the development of effective traffic safety policies that reflect the characteristics of the city bus industry.

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

Cited by 17 publications

References 43 publications

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

Investigating the Factors Influencing Pedestrian–Vehicle Crashes by Age Group in Seoul, South Korea: A Hierarchical Model

Analysis of Intrinsic Factors Leading to Aggressive Driving Behavior to Derive Safety Policy Implications for Bus Drivers

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