Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances

Fountas, Grigorios; Anastasopoulos, Panagiotis Ch.

doi:10.1016/j.amar.2018.09.002

Cited by 70 publications

(36 citation statements)

References 59 publications

(104 reference statements)

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“…Approaches including a zero-inflated hierarchical ordered probit and random parameter logit model were utilized to figure out the correlations. The models explain the relation better and show a reasonable level of accuracy from less detailed data [20,21].…”

Section: Traffic Accident Forecasting Modelmentioning

confidence: 82%

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

2019

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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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Section: Traffic Accident Forecasting Modelmentioning

confidence: 82%

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

2019

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“…Oh [20] established ordered probit regression models to determine the probabilities of injury severity degrees for all types of crashes. A zero-inflated hierarchical ordered probit approach was proposed to forecast accuracy improvements in terms of accident severities [21]. Jang et al [22] developed a multi-level Poisson regression model to capture the association between external environment and crashes.…”

Section: Safety Analysis On Roadwaysmentioning

confidence: 99%

Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks

Zhu

Zhang

et al. 2019

Sustainability

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Crashes that occur on curved roadways are often more severe than straight road accidents. Previously, most studies focused on the associations between curved sections and roadway geometric characteristics. In this study, significant factors such as driver behavior, roadway features, vehicle factors, and environmental characteristics are identified and involved in analyzing traffic accident severity. Bayesian network analysis was conducted to deal with data, to explore the associations between variables, and to make predictions using these relationships. The results indicated that factors including point of impact, site of location, accident side of road, alcohol/drugs condition, etc., are relatively critical in crashes on horizontal curves. Accident severity increases when crashes occur on bridges. The sensitivity of accident severity to vehicle use, traffic control, point of impact, and alcohol/drugs condition is relatively high. Moreover, a combination of negative factors will aggravate accident severities. The results also proposed some suggestions regarding the design of vehicles, as well as the construction and improvement of curved roadways.

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“…Considering the cyclist database for Lisbon, oversampling is the best resampling technique for both classifiers. Besides, this Gender (41) Road Markings (16) Time (11) Road Markings (23) Gender (20) Age (18) Road Markings (42) Luminosity (36) Age (11) Gender (28) Road Markings (25) Age (20) FOR PEER REVIEW 8 of 17 explore and compare the results of two supervised classification techniques in order to entify which variables can significantly affect pedestrian and cyclist injury severity when olved in a motor vehicle crash. e three resampling techniques were applied to the datasets, resulting in six different datasets city and the overall perspective.…”

Section: Datasetsmentioning

confidence: 99%

“…e best results (highlighted in (16) Month (30) Gender (18) Time (17) Luminosity (24) Weather (19) Weekday (13) Time (13) Road Conditions (19) Age (18) Luminosity (16) Considering the analysis for pedestrians ( Table 5), for Aveiro, Lisbon and the overall dataset, oversampling is the best resampling technique applied for the logistic regression model. Only Porto presented ROSE as the best resampling approach when applied to this classifier.…”

Section: Datasetsmentioning

confidence: 99%

“…On the other hand, age group and temporal variables (month, weekday and time period) showed to be relevant to predict the severity of a cyclist injury when involved in a crash.Predicting road crashes and finding patterns from crash registrations is an important step to develop safety measures. Among a lot of research into the evaluation of crash likelihood and frequency, several studies have focused on factors affecting the injury severity [9][10][11][12][13][14][15][16][17][18]. Notwithstanding the personal injury, the economic and societal cost of crashes varies substantially based on the severity level of injury.…”

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confidence: 99%

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A Rare Event Modelling Approach to Assess Injury Severity Risk of Vulnerable Road Users

Vilaça

Macedo

Coelho

2019

Safety

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Vulnerable road users (VRUs) represent a large portion of fatalities and injuries occurring on European Union roads. It is therefore important to address the safety of VRUs, particularly in urban areas, by identifying which factors may affect the injury severity level that can be used to develop countermeasures. This paper aims to identify the risk factors that affect the severity of a VRU injured when involved in a motor vehicle crash. For that purpose, a comparative evaluation of two machine learning classifiers—decision tree and logistic regression—considering three different resampling techniques (under-, over- and synthetic oversampling) is presented, comparing both imbalanced and balanced datasets. Crash data records were analyzed involving VRUs from three different cities in Portugal and six years (2012–2017). The main conclusion that can be drawn from this study is that oversampling techniques improve the ability of the classifiers to identify risk factors. On the one hand, this analysis revealed that road markings, road conditions and luminosity affect the injury severity of a pedestrian. On the other hand, age group and temporal variables (month, weekday and time period) showed to be relevant to predict the severity of a cyclist injury when involved in a crash.

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Analysis of accident injury-severity outcomes: The zero-inflated hierarchical ordered probit model with correlated disturbances

Cited by 70 publications

References 59 publications

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

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

Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks

A Rare Event Modelling Approach to Assess Injury Severity Risk of Vulnerable Road Users

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