Comparison of Machine Learning Algorithms for Predicting Traffic Accident Severity

AlMamlook, Rabia Emhamed; Kwayu, Keneth Morgan; Al-Kasisbeh, Maha Reda; Frefer, Abdulbaset

doi:10.1109/jeeit.2019.8717393

Cited by 92 publications

(40 citation statements)

References 9 publications

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“…Several studies were related to crash severity prediction with MLAs. In these applications, the output classes can be two, such as Property Damage Only (PDO) accidents and Fatal+Injury (F + I) accidents [21][22][23], three [24][25][26], four [27][28][29], or five [30][31][32]. The authors suggested different MLAs to achieve the purpose: decision tree [23,24,29,30], KNN [30], LR [21,22], and RF [21,28,30,32].…”

Section: Machine Learning Algorithms In Road Safety Analysesmentioning

confidence: 99%

“…In these applications, the output classes can be two, such as Property Damage Only (PDO) accidents and Fatal+Injury (F + I) accidents [21][22][23], three [24][25][26], four [27][28][29], or five [30][31][32]. The authors suggested different MLAs to achieve the purpose: decision tree [23,24,29,30], KNN [30], LR [21,22], and RF [21,28,30,32]. Other standard MLAs used for the same purpose are support vector machines [22,28,[30][31][32], neural networks [14,[27][28][29], and naïve Bayes classifiers [21,33].…”

Section: Machine Learning Algorithms In Road Safety Analysesmentioning

confidence: 99%

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Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms

Fiorentini

Losa

2020

Infrastructures

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Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that crash datasets are acutely imbalanced. Overlooking this fact generally leads to weak classifiers for predicting the minority class (crashes with higher severity). In this paper, in order to handle imbalanced accident datasets and provide a better prediction for the minority class, the random undersampling the majority class (RUMC) technique is used. By employing an imbalanced and a RUMC-based balanced training set, we propose the calibration, validation, and evaluation of four different crash severity predictive models, including random tree, k-nearest neighbor, logistic regression, and random forest. Accuracy, true positive rate (recall), false positive rate, true negative rate, precision, F1-score, and the confusion matrix have been calculated to assess the performance. Outcomes show that RUMC-based models provide an enhancement in the reliability of the classifiers for detecting fatal crashes and those causing injury. Indeed, in imbalanced models, the true positive rate for predicting fatal crashes and those causing injury spans from 0% (logistic regression) to 18.3% (k-nearest neighbor), while for the RUMC-based models, it spans from 52.5% (RUMC-based logistic regression) to 57.2% (RUMC-based k-nearest neighbor). Organizations and decision-makers could make use of RUMC and machine learning algorithms in predicting the severity of a crash occurrence, managing the present, and planning the future of their works.

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Section: Machine Learning Algorithms In Road Safety Analysesmentioning

confidence: 99%

Section: Machine Learning Algorithms In Road Safety Analysesmentioning

confidence: 99%

Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms

Fiorentini

Losa

2020

Infrastructures

View full text Add to dashboard Cite

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“…The author's preference model better performs than the single classifiers [5]. AlMamlook et al [6] used AdaBoost, Nave Bayes, Logistic Regression and random forest to get determinant factors and to identify high risky highways for Michigan traffic Agencies. Performance measurement ROC, AUC, Precision and recall and F1-score were applied to evaluate models.…”

Section: Litrature Reviewmentioning

confidence: 99%

Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach

Yassin

Pooja

2020

SN Appl. Sci.

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Road accident severity is a major concern of the world, particularly in underdeveloped countries. Understanding the primary and contributing factors may combat road traffic accident severity. This study identified insights and the most significant target specific contributing factors for road accident severity. To get the most determinant road accident variables, a hybrid K-means and random forest (RF) approaches developed. K-means extract hidden information from road accident data and creates a new feature in the training set. The distance between each cluster and the joining line of k1 and k9 calculated and selected maximum value as k. k is an optimal value for the partition of the training set. RF employed to classify severity prediction. After comparing with other classification techniques, the result revealed that among classification techniques, the proposed approach disclosed an accuracy of 99.86%. The target-specific model interpretation result showed that driver experience and day, light condition, driver age, and service year of the vehicle were the strong contributing factors for serious injury, light injury, and fatal severity, respectively. The outcome demonstrates the predictive supremacy of the approach in road accident prediction. Road transport and insurance agencies will be benefited from the study to develop road safety strategies.

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“…Emhamed et al implemented four ML algorithms, including RF, DT, Naive Bayes (NB), and logistic regression (LR), to predict crash severity prediction [ 78 ]. Findings indicated that all the algorithms yielded reasonable model performance.…”

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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Comparison of Machine Learning Algorithms for Predicting Traffic Accident Severity

Cited by 92 publications

References 9 publications

Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms

Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms

Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach

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

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