An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier

Chen, Cong; Zhang, Guohui; Yang, Jinfu; Milton, John; Alcántara, Adélamar

doi:10.1016/j.aap.2016.02.002

Cited by 109 publications

(39 citation statements)

References 68 publications

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“…For other median types, two to four traffic lanes had 16.2% fatal rear-end crashes. With regard to leaf node, envi_light was found to be in accordance with Chen et al [22], who found that collisions occurring at night both with light and no light have a greater chance of fatality than collisions occurring during the daytime.…”

Section: Conflicts Of Interestsupporting

confidence: 85%

“…Previous research has found that factors causing death in rear-end crashes included driver characteristics a ecting braking, such as gender, age, and alcohol or substance abuse [21]. Use of a seatbelt has been found to be another important contributing factor to rear-end fatalities [22]. Vehicle type is an important factor in all accident types [23], but especially in rear-end crashes.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach

Champahom

Jomnonkwao

Chatpattananan

et al. 2019

Journal of Advanced Transportation

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Objective. Among crash types on ai highways, rear-end crashes have been found to cause the largest number of fatalities. is study aims to nd ways to decrease rear-end crashes and fatal rear-end crashes. Methods. Classi cation and regression tree (CART) was used to analyze the complicated relationship of variables of big data. e analysis was conducted by creating two models: (1) a model which indicates the causes of rear-end crashes by applying Quasi-Induced Exposure to at-fault driver characteristics; (2) a determined model which studies fatal crashes. Results. Predictor variables in the model of at-fault and not-at-fault drivers found that driver age is most signi cant, followed by number of lanes and median opening area. For the mode of fatality, the use of safety equipment was found to be of most importance. Conclusion. e model results can be used to develop guidelines for public awareness programs for motorists and to propose policy changes to the Department of Highway in order to reduce the severity of rear-end crashes. Moreover, this paper discusses the variables that may result in both the perspective of rear-end crash number and the fatality rate of rear-end crashes as strategies in future research.

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Section: Conflicts Of Interestsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach

Champahom

Jomnonkwao

Chatpattananan

et al. 2019

Journal of Advanced Transportation

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“…In addition, all types of vehicles are more likely to be involved with serious injury, when in a collision with a large truck (gross weight > 3.5 ton). Heavy vehicle involvement would increase the likelihood of drivers sustaining fatal injuries 2 . Therefore, as a kind of frequent and serious crash, rear-end crashes involving trucks require particular investigation to explore the contributing factors and prevention countermeasure of these crashes.…”

Section: Introductionmentioning

confidence: 99%

Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China

Yuan

Theofilatos

et al. 2017

Chinese Journal of Traumatology

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PurposeRear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the critical factors for occupant injury severity in the specific rear-end crash type involving trucks as the front vehicle (FV).MethodsThis paper investigated crashes occurred from 2011 to 2013 in Beijing area, China and selected 100 qualified cases i.e., rear-end crashes involving trucks as the FV. The crash data were supplemented with interviews from police officers and vehicle inspection. A binary logistic regression model was used to build the relationship between occupant injury severity and corresponding affecting factors. Moreover, a multinomial logistic model was used to predict the likelihood of fatal or severe injury or no injury in a rear-end crash.ResultsThe results provided insights on the characteristics of driver, vehicle and environment, and the corresponding influences on the likelihood of a rear-end crash. The binary logistic model showed that drivers' age, weight difference between vehicles, visibility condition and lane number of road significantly increased the likelihood for severe injury of rear-end crash. The multinomial logistic model and the average direct pseudo-elasticity of variables showed that night time, weekdays, drivers from other provinces and passenger vehicles as rear vehicles significantly increased the likelihood of rear drivers being fatal.ConclusionAll the abovementioned significant factors should be improved, such as the conditions of lighting and the layout of lanes on roads. Two of the most common driver factors are drivers' age and drivers' original residence. Young drivers and outsiders have a higher injury severity. Therefore it is imperative to enhance the safety education and management on the young drivers who steer heavy duty truck from other cities to Beijing on weekdays.

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“…ML methods are more flexible with no or fewer model assumptions for input variables, and also have better fitting characteristics. Some of the commonly used ML approaches used in crash injury severity prediction include artificial neural networks (ANN) [ 58 , 59 , 60 ], random forest [ 54 , 61 , 62 ], support vector machines (SVM) [ 51 , 63 , 64 ], naïve Bayes [ 65 , 66 , 67 ], K-means clustering (KC) [ 68 , 69 , 70 ], and decision trees (DT) [ 71 , 72 , 73 ].…”

Section: Related Workmentioning

confidence: 99%

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

Jamal

Umer

2020

IJERPH

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A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigation strategies. This study proposes an improved feed-forward neural network (FFNN) model for predicting injury severity associated with individual crashes using three years (2017–2019) of crash data collected along 15 rural highways in the Kingdom of Saudi Arabia (KSA). A total of 12,566 crashes were recorded during the study period with a binary injury severity outcome (fatal or non-fatal injury) for the variable to be predicted. FFNN architecture with back-propagation (BP) as a training algorithm, logistic as activation function, and six number of hidden neurons in the hidden layer yielded the best model performance. Results of model prediction for the test data were analyzed using different evaluation metrics such as overall accuracy, sensitivity, and specificity. Prediction results showed the adequacy and robust performance of the proposed method. A detailed sensitivity analysis of the optimized NN was also performed to show the impact and relative influence of different predictor variables on resulting crash injury severity. The sensitivity analysis results indicated that factors such as traffic volume, average travel speeds, weather conditions, on-site damage conditions, road and vehicle type, and involvement of pedestrians are the most sensitive variables. The methods applied in this study could be used in big data analysis of crash data, which can serve as a rapid-useful tool for policymakers to improve highway safety.

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An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier

Cited by 109 publications

References 68 publications

Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach

Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach

Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

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