An Ensemble of Multiple Boosting Methods Based on Classifier-Specific Soft Voting for Intelligent Vehicle Crash Injury Severity Prediction

2020 ◽

Vol 2674 (5) ◽

pp. 452-464 ◽

Cited By ~ 1

Author(s):

Mohammad Razaur Rahman Shaon ◽

Xiao Qin

Keyword(s):

Injury Severity ◽

Model Performance ◽

Severe Injuries ◽

Crash Data ◽

Severity Prediction ◽

Safety Interventions ◽

Driving Task ◽

Driver Error ◽

Crash Characteristics ◽

Crash Injury Severity

Unsafe driving behaviors, driver limitations, and conditions that lead to a crash are usually referred to as driver errors. Even though driver errors are widely cited as a critical reason for crash occurrence in crash reports and safety literature, the discussion on their consequences is limited. This study aims to quantify the effect of driver errors on crash injury severity. To assist this investigation, driver errors were categorized as sequential events in a driving task. Possible combinations of driver error categories were created and ranked based on statistical dependences between error combinations and injury severity levels. Binary logit models were then developed to show that typical variables used to model injury severity such as driver characteristics, roadway characteristics, environmental factors, and crash characteristics are inadequate to explain driver errors, especially the complicated ones. Next, ordinal probit models were applied to quantify the effect of driver errors on injury severity for rural crashes. Superior model performance is observed when driver error combinations were modeled along with typical crash variables to predict the injury outcome. Modeling results also illustrate that more severe crashes tend to occur when the driver makes multiple mistakes. Therefore, incorporating driver errors in crash injury severity prediction not only improves prediction accuracy but also enhances our understanding of what error(s) may lead to more severe injuries so that safety interventions can be recommended accordingly.

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Study on Crash Injury Severity Prediction of Autonomous Vehicles for Different Emergency Decisions Based on Support Vector Machine Model

Electronics ◽

10.3390/electronics7120381 ◽

2018 ◽

Vol 7 (12) ◽

pp. 381 ◽

Cited By ~ 6

Author(s):

Yaping Liao ◽

Junyou Zhang ◽

Shufeng Wang ◽

Sixian Li ◽

Jian Han

Keyword(s):

Support Vector Machine ◽

Autonomous Vehicles ◽

Injury Severity ◽

Prediction Models ◽

Motor Vehicle ◽

Support Vector ◽

Severity Prediction ◽

Emergency Decision ◽

The Impact ◽

Crash Injury Severity

Motor vehicle crashes remain a leading cause of life and property loss to society. Autonomous vehicles can mitigate the losses by making appropriate emergency decision, and the crash injury severity prediction model is the basis for autonomous vehicles to make decisions in emergency situations. In this paper, based on the support vector machine (SVM) model and NASS/GES crash data, three SVM crash injury severity prediction models (B-SVM, T-SVM, and BT-SVM) corresponding to braking, turning, and braking + turning respectively are established. The vehicle relative speed (REL_SPEED) and the gross vehicle weight rating (GVWR) are introduced into the impact indicators of the prediction models. Secondly, the ordered logit (OL) and back propagation neural network (BPNN) models are established to validate the accuracy of the SVM models. The results show that the SVM models have the best performance than the other two. Next, the impact of REL_SPEED and GVWR on injury severity is analyzed quantitatively by the sensitivity analysis, the results demonstrate that the increase of REL_SPEED and GVWR will make vehicle crash more serious. Finally, the same crash samples under normal road and environmental conditions are input into B-SVM, T-SVM, and BT-SVM respectively, the output results are compared and analyzed. The results show that with other conditions being the same, as the REL_SPEED increased from the low (0–20 mph) to middle (20–45 mph) and then to the high range (45–75 mph), the best emergency decision with the minimum crash injury severity will gradually transition from braking to turning and then to braking + turning.

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Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17155497 ◽

2020 ◽

Vol 17 (15) ◽

pp. 5497

Author(s):

Khaled Assi ◽

Syed Masiur Rahman ◽

Umer Mansoor ◽

Nedal Ratrout

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Clustering Algorithm ◽

Injury Severity ◽

Support Vector ◽

Severity Level ◽

Feed Forward ◽

Fuzzy C Means ◽

Severity Prediction ◽

Crash Injury Severity

Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily identified with little investigation on crash sites were used as an input so that the trauma center can predict the crash severity level based on the initial information provided from the crash site and prepare accordingly for the treatment of the victims. The input parameters mainly include vehicle attributes and road condition attributes. This study used the crash database of Great Britain for the years 2011–2016. A random sample of crashes representing each year was used considering the same share of severe and non-severe crashes. The models were compared based on injury severity prediction accuracy, sensitivity, precision, and harmonic mean of sensitivity and precision (i.e., F1 score). The SVM-FCM model outperformed the other developed models in terms of accuracy and F1 score in predicting the injury severity level of severe and non-severe crashes. This study concluded that the FCM clustering algorithm enhanced the prediction power of FNN and SVM models.

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