Traffic Accidents Classification and Injury Severity Prediction

Cuenca, Laura García; Puertas, Enrique; Aliane, Nourdine; Andrés, Javier Fernández

doi:10.1109/icite.2018.8492545

Cited by 24 publications

(8 citation statements)

References 12 publications

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“…Cuenca et al [14] compared the performance of Naive Bayes, Deep Learning and Gradient Boosting to predict the severity of injury for Spanish road accidents. Their work reported that Deep Learning outperform other methods…”

Section: Literature Reviewmentioning

confidence: 99%

Road Traffic Accidents Injury Data Analytics

Nour¹,

Naseer²,

Alkazemi³

et al. 2020

IJACSA

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Section: Literature Reviewmentioning

confidence: 99%

Road Traffic Accidents Injury Data Analytics

Nour¹,

Naseer²,

Alkazemi³

et al. 2020

IJACSA

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“…Gradient boosting trees, deep learning, and naïve Bayes machine learning techniques were used to predict the severity of road traffic collisions using six years' worth of raw data from the Spanish traffic agency. The study concluded that the deep learning model outperformed the other two models in terms of accuracy and precision [35].…”

Section: Literature Reviewmentioning

confidence: 93%

Crash Severity Prediction Using Two-Layer Ensemble Machine Learning Model for Proactive Emergency Management

et al. 2020

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Many unfortunate victims in road traffic crashes do not receive ideal treatment because their injury severity is not understood at an early stage. Swift crash severity prediction enables trauma and emergency centers to estimate the potential damage resulting from a road traffic crash and accordingly dispatch the proper emergency units to provide appropriate emergency treatment. A two-layer ensemble machine learning model is proposed in this study to predict road traffic crash severity. The first layer integrates four base machine learning models: k-nearest neighbor, decision tree, adaptive boosting, and support vector machine; the second layer classifies the crash severity based on the feedforward neural network model. The models are developed using road traffic crash data of road intersections over 6 years (2011-2016) obtained from Great Britain's Department of Transport online database. Only the crash features that can be instantaneously and easily obtained are used as an input. To simplify the two-layer ensemble model, principal component analysis technique is used for dimensionality reduction in the second layer of the model. The performance of the two-layer ensemble model is compared with five base models: k-nearest neighbor, decision tree, adaptive boosting, support vector machine, and feedforward neural network. The prediction results reveal that the two-layer ensemble model outperforms the five base classification models based on two performance indicators: testing accuracy and F1 score. The transferability of the developed model is tested using the 3-year crash dataset for Canada obtained from the National Crash Database Online. The outcome indicates that the two-layer ensemble model shows the best performance for the Canadian dataset also. The proposed two-layer ensemble model would be beneficial in predicting crash severity with high accuracy based on limited initial crash information obtained from the crash location. Using this information, trauma centers would be able to prepare for appropriate and prompt medical treatment.

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“…It was found that the RF model outperformed the MNL model. In their study, Cuenca et al [ 21 ] made a comparison between different ML techniques, such as deep learning, naïve Bayes, and gradient boosted trees, based on their prediction performance of RTC severity. Prediction accuracy results revealed that the deep learning model outperformed the other models.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models

Assi

2020

IJERPH

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The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal component analysis (PCA) with multilayer perceptron neural networks (MLP-NN) and support vector machines (SVM) in predicting RTC severity. PCA shows that the first nine components have an eigenvalue greater than one. The cumulative variance percentage explained by these principal components was found to be 67%. The prediction accuracies of the models developed using the original attributes were compared with those of the models developed using principal components. It was found that the testing accuracies of MLP-NN and SVM increased from 64.50% and 62.70% to 82.70% and 80.70%, respectively, after using principal components. The proposed models would be beneficial to trauma centers in predicting crash severity with high accuracy so that they would be able to prepare for appropriate and prompt medical treatment.

show abstract

Traffic Accidents Classification and Injury Severity Prediction

Cited by 24 publications

References 12 publications

Road Traffic Accidents Injury Data Analytics

Road Traffic Accidents Injury Data Analytics

Crash Severity Prediction Using Two-Layer Ensemble Machine Learning Model for Proactive Emergency Management

Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models

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