Traffic accident severity analysis with rain-related factors using structural equation modeling – A case study of Seoul City

Lee, Jong-Hak; Chae, Junghyo; Yoon, Taekwan; Yang, Hojin

doi:10.1016/j.aap.2017.12.013

Cited by 78 publications

(34 citation statements)

References 18 publications

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“…Many models have been proposed for predicting the severity of traffic accidents, such as structural equation models, logit/probit models, logistic regression models and Bayesian networks. [10][11][12][13][14][15][16] Many studies have explored multiple aspects of the causes of night-time vehicle accidents under low illumination conditions, and most of these studies have been conducted from the perspective of drivers. Sullivan and Flannagan 17 used the ratio of night-time to daytime accidents to evaluate the association between nighttime accidents and driver characteristics and found that young drivers had a significantly greater risk of fatal accidents at night than older drivers.…”

Section: Introductionmentioning

confidence: 99%

Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Liu

Wang

et al. 2019

Advances in Mechanical Engineering

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Night-time vehicle accidents under low illumination conditions are frequent and serious, and they have attracted widespread attention. The objective of this study was to explore how various factors affect night-time vehicle accidents using data collected from a city in China. Combined with logistic model theory, the occurrence or absence of a night-time fatal accident was set as the dependent variable. A total of 10 variables, including the accident site, road type and road surface conditions, were selected as independent factors. Based on 2106 valid night-time vehicle accidents, a binomial logistic model was established to evaluate the impact of contributing factors on the severity of these accidents. The results show that the accident site, accident type and presence of a median divider are important factors that affect the severity of night-time vehicle accidents under low illumination conditions. The probability of fatal night-time accidents on road segments is 2.387 times that at intersections. The probabilities of fatal single-vehicle and vehicle-pedestrian night-time accidents are also greater than that of fatal vehicle-vehicle night-time accidents, by factors of 7.591 and 1.749, respectively. The probability of fatal night-time accidents on roads with median dividers is 3.273 times greater than that on roads without median dividers.

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Section: Introductionmentioning

confidence: 99%

Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Liu

Wang

et al. 2019

Advances in Mechanical Engineering

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“…However, this kind of inter-relationships cannot be discovered by a single regression model, such as the multinomial logit model, ordered logit/probit model, and the mixed logit model [ 21 ]. SEM is required to examine both the direct and indirect effects of contributing factors on the outcome variable, which has been employed in many studies relating to traffic crashes [ 10 , 11 , 12 , 13 ]. Using SEM with different model structures, Wang and Qin [ 12 ] explored the direct and indirect contribution of various factors to the severity of single-vehicle crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Lee et al [ 10 ] analyzed the rainfall and traffic accident data from 2007 to 2015 using SEM with the aim to identify the relationship between the accident severity and rain-related factors. Shaaban et al [ 13 ] employed SEM to examine the indirect relationship between injury severity of red-light-running-related crashes and some contributing factors. These previous studies provided good examples for the application of SEM in the present study.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, the pedestrian characteristic may have indirect effects on the injury severity through influencing the pre-crash behavior. Those inter-relationships could be untangled by using the method known as path analysis, a statistical analysis method for Structural Equation Modeling (SEM), which has been applied for many studies concerning driving behaviors and traffic crashes [ 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Examining Injury Severity of Pedestrians in Vehicle–Pedestrian Crashes at Mid-Blocks Using Path Analysis

Peng

Chen

2020

IJERPH

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Walking is a sustainable mode of transport which has well established health and environmental benefits. Unfortunately, hundreds of thousands of pedestrians lose their lives each year over the world due to involvement in road traffic crashes, and mid-blocks witness a significant portion of pedestrian fatalities. This study examined the direct and indirect effects of various contributing factors on the pedestrian injury severity in vehicle–pedestrian crashes at mid-blocks. Data of vehicle–pedestrian crashes during 2002–2009 were extracted from the NASS-GES, with pre-crash behaviors and injury severity included. The SEM path analysis method was applied to uncover the inter-relationships between the pedestrian injury severity and various explanatory variables. Both the direct and indirect effects of these explanatory variables on the pedestrian injury severity were calculated based on the marginal effects in the multinomial and ordered logit models. The results indicate some variables including number of road lanes and the age of pedestrian have indirect impacts on the injury severity through influencing the pre-crash behaviors. Although most indirect effects are relatively small compared with the direct effects, the results in this study still provide some valuable information to improve the overall understanding of pedestrian injury severity at mid-blocks.

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“…Other studies have investigated the relationships between traffic accidents and the designed road geometry and weather conditions, especially rainfall, using statistical methods [12][13][14][15][16]. It is revealed that rain factors including water depth are strongly correlated with traffic accident severity using the structural equation model [17]. On the other hand, other studies revealed that the effect of precipitation does not affect directly to accident severity, but increasing accident frequency does [18,19].…”

Section: Traffic Accident Forecasting Modelmentioning

confidence: 99%

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

2019

Self Cite

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There have been numerous studies on traffic accidents and their severity, particularly in relation to weather conditions and road geometry. In these studies, traditional statistical methods have been employed, such as linear regression, logistic regression, and negative binomial regression modeling, which are the most common linear and non-linear regression analysis methods. In this research, machine learning architecture was applied to this problem using the random forest, artificial neural network, and decision tree techniques to ascertain the strengths and weaknesses of these methods. Three data sets were used: road geometry data, precipitation data, and traffic accident data over nine years corresponding to the Naebu Expressway, which is located in Seoul, Korea. For the model evaluation, three measures were employed: the out-of-bag estimate of error rate (OOB), mean square error (MSE), and root mean square error (RMSE). The low mean OOB, MSE, and RMSE observed in the results obtained using the proposed random forest model demonstrate its accuracy.

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Traffic accident severity analysis with rain-related factors using structural equation modeling – A case study of Seoul City

Cited by 78 publications

References 18 publications

Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Examining Injury Severity of Pedestrians in Vehicle–Pedestrian Crashes at Mid-Blocks Using Path Analysis

Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

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