Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms
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. Overlo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/5/7/61 |
| _version_ | 1797561896731148288 |
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| author | Nicholas Fiorentini Massimo Losa |
| author_facet | Nicholas Fiorentini Massimo Losa |
| author_sort | Nicholas Fiorentini |
| collection | DOAJ |
| description | 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, <i>F</i><sub>1</sub>-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. |
| first_indexed | 2024-03-10T18:21:06Z |
| format | Article |
| id | doaj.art-b3da26cdab2944208f5a8313c9b6364f |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-10T18:21:06Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-b3da26cdab2944208f5a8313c9b6364f2023-11-20T07:21:27ZengMDPI AGInfrastructures2412-38112020-07-01576110.3390/infrastructures5070061Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning AlgorithmsNicholas Fiorentini0Massimo Losa1Department of Civil and Industrial Engineering (DICI), Engineering School of the University of Pisa, Largo Lucio Lazzarino 1, 56126 Pisa, ItalyDepartment of Civil and Industrial Engineering (DICI), Engineering School of the University of Pisa, Largo Lucio Lazzarino 1, 56126 Pisa, ItalyCrash 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, <i>F</i><sub>1</sub>-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.https://www.mdpi.com/2412-3811/5/7/61crash severitymachine learning classification algorithmsrandom undersampling the majority classrandom classification treek-nearest neighborrandom forest |
| spellingShingle | Nicholas Fiorentini Massimo Losa Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms Infrastructures crash severity machine learning classification algorithms random undersampling the majority class random classification tree k-nearest neighbor random forest |
| title | Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms |
| title_full | Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms |
| title_fullStr | Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms |
| title_full_unstemmed | Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms |
| title_short | Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms |
| title_sort | handling imbalanced data in road crash severity prediction by machine learning algorithms |
| topic | crash severity machine learning classification algorithms random undersampling the majority class random classification tree k-nearest neighbor random forest |
| url | https://www.mdpi.com/2412-3811/5/7/61 |
| work_keys_str_mv | AT nicholasfiorentini handlingimbalanceddatainroadcrashseveritypredictionbymachinelearningalgorithms AT massimolosa handlingimbalanceddatainroadcrashseveritypredictionbymachinelearningalgorithms |