Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

Wang, Zhengwu; Huang, Shuai; Wang, Jie; Sulaj, Denisa; Hao, Wei; Kuang, Aiwu

doi:10.1016/j.jsr.2020.12.009

Cited by 44 publications

(24 citation statements)

References 22 publications

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“…En esencia, el MCAD selecciona iterativamente el atributo y el valor que puede dividir un conjunto de muestras en dos grupos, minimizando la variabilidad dentro de cada subgrupo y maximizando el contraste entre los grupos. A diferencia de las redes neuronales artificiales (RNA), en los MCAD, el proceso de decisión se genera explícitamente y la estructura del árbol presenta información sobre dicho proceso, es simple de entender y no es paramétrico, tampoco requiere que los datos asociados con una clase particular en un atributo sigan una distribución normal (Wang et al, 2021). Como se mencionó anteriormente, la información fue recolectada a partir de una encuesta virtual aplicada en diferentes universidades colombianas, tanto públicas como privadas (Universidad Pontifica Bolivariana, Universidad Católica Luis Amigó, Universidad Nacional de Colombia, Universidad de Antioquia, Universidad de Medellín y Universidad EAFIT), a estudiantes de diferentes programas académicos.…”

Section: Métodounclassified

Depresión en estudiantes universitarios derivada del Covid-19: un modelo de clasificación

López

Vera

2021

cuad.hispanoam.psicol

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Las estrategias gubernamentales adoptadas a nivel mundial como medida de prevención frente a la emergencia sanitaria generada por el COVID-19, han despertado el interés de la comunidad científica por conocer los efectos de las mismas sobre la salud mental. El objetivo de esta investigación fue desarrollar un modelo de clasificación para pronosticar depresión en estudiantes universitarios por factores de estrés asociados a la pandemia. Se desarrolló un Modelo de Clasificación con Árboles de Decisión (MCAD) a partir de los resultados de una encuesta de percepción con preguntas tipo escala Likert, la cual fue aplicada a una muestra de 833 estudiantes universitarios de diferentes programas académicos de Colombia. La variable dependiente del modelo fue la presencia o ausencia de depresión, y las respuestas de 700 estudiantes se emplearon para el entrenamiento y 133 para la prueba. Se concluye que el MCAD es válido para pronosticar depresión; tuvo una tasa de precisión del 87% en la muestra de prueba. Finalmente, se halló que los factores que más influyen en el desarrollo de estados depresivos en el contexto de la pandemia generada por el COVID-19 en estudiantes universitarios son: estado civil, sexo, edad, problemas educativos e información proveniente de los medios de comunicación.

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Section: Métodounclassified

Depresión en estudiantes universitarios derivada del Covid-19: un modelo de clasificación

López

Vera

2021

cuad.hispanoam.psicol

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“…Rudisill et al [35] used logistic regression model to explore the effect of driver age, collision location, and other factors on the cause of traffic crashes. Wang et al [36] applied a classification tree-based logistic regression model to identify the risk factors affecting crash injury severity for different types of e-bike riders.…”

Section: Relevant Studies On the Modelling Approach For Crashmentioning

confidence: 99%

Exploring Factors Contributing to Crash Injury Severity in the Rural-Urban Fringe of the Central City

Wang

Liu

Dong³

et al. 2021

Journal of Advanced Transportation

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To analyze the risk factors influencing the crash injury severity in rural-urban fringes, crash data in rural-urban fringes were collected from Harbin, China. Four risk factors, namely, time of day, vehicle type, road feature, and crash type, were investigated associated with the severity of rural-urban fringe crashes. The crash injury severity was divided into two categories, including fatal and nonfatal crash. The logistic regression was applied to explore the relationships between the severity outcomes and time of day, vehicle type, road feature, and crash type. The test methods of goodness-of-fit and badness-of-fit are conducted to examine the validity of estimation results. The results show considerable matching of the number of different crash types between calculated results and actual data. Compared with the other influencing factors, the time of day is significant factor for crash injury severity based on the study. As such, the proposed calibration procedure and the factors of choice are recommended as a validated approach to analyze and identify the main factors influencing crash injury severity in rural-urban fringes.

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“…It shows that these three types of vehicles will significantly increase the probability of fatal outcomes by 5.62%, 13.35%, and 78.80%, respectively. Study [53] found that for crashes colliding with the truck, the likelihood of riders suffering from severe injury increases by 9.5% on average. e reason may be related to the characteristics of large vehicles such as heavy weight, and also related to the obvious visual blind spots.…”

Section: Effects Of Vehicle Characteristicsmentioning

confidence: 99%

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

Wang

Cheng

et al. 2021

Journal of Advanced Transportation

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Electric bicyclists are vulnerable road users and play an important role in traffic safety. The focus of this research is on analyzing cyclists’ injury severity in vehicle-electric bicycle collisions. It is an exploratory analysis that was conducted based on samples obtained from video data provided by the police of Xi’an China. Three types of severity include fatal, injury, and property-damage-only (PDO). A random parameter logit (RPL) model was specified to gain more insights into factors related to the injury severity level, including human behaviors, vehicle characteristics, roadway attributes, and environmental conditions. Some factors not included in previous research were introduced into this study, especially precrash behaviors of drivers and cyclists. The direct pseudo-elasticity effects of variables were compared to investigate the stability of individual parameter estimates on the severity categories. The results indicated that variables that significantly increment the probability of fatal accidents were as follows: driver violation behaviors (speeding, red-light violation, driving in the opposite direction), cyclist violation behaviors (speeding, red-light violation), day of time (nighttime), visibility restrictions (fixed obstacles), and vehicle type (larger bus, small truck, and larger truck). Based on these findings, we suggested measures such as strengthening law enforcement by installing cameras, implementing zero tolerance for cyclist violations, promoting education by completing training courses for cyclists, and enhancing traffic safety awareness through educational activities. The research results can provide a theoretical basis for formulating strategies to improve cyclist safety.

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Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

Cited by 44 publications

References 22 publications

Depresión en estudiantes universitarios derivada del Covid-19: un modelo de clasificación

Depresión en estudiantes universitarios derivada del Covid-19: un modelo de clasificación

Exploring Factors Contributing to Crash Injury Severity in the Rural-Urban Fringe of the Central City

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

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