Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads
The aim of the current research was to develop models to predict the severity of accidents on rural roads in Tehran province, Iran. In this regard, using accident data from 2017 to 2020, the machine learning algorithms, including multiple logistic regression, multilayer perceptron neural network (ML...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/4857013 |
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| _version_ | 1832563307792302080 |
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| author | Mohammad Habibzadeh Mahmoud Ameri Hassan Ziari Neda Kamboozia Seyede Mojde Sadat Haghighi |
| author_facet | Mohammad Habibzadeh Mahmoud Ameri Hassan Ziari Neda Kamboozia Seyede Mojde Sadat Haghighi |
| author_sort | Mohammad Habibzadeh |
| collection | DOAJ |
| description | The aim of the current research was to develop models to predict the severity of accidents on rural roads in Tehran province, Iran. In this regard, using accident data from 2017 to 2020, the machine learning algorithms, including multiple logistic regression, multilayer perceptron neural network (MLPNN), and radial basis function neural network (RBFNN) models, as well as statistical methods, including Kolmogorov–Smirnov test, Friedman test, and factor analysis, were implemented to determine the contributory factors in the severity of accidents. Thus, nine variables affecting the severity of accidents were considered in modeling, and then the effect of each variable was calculated. By comparing the results of artificial neural network (ANN) models and the Friedman test, it was indicated that the human factor had a remarkable effect on accident severity. In addition, both machine learning and statistical methods can be served as guidance for safety authorities to provide safety solutions, thereby leading to reducing accidents. Finally, the performances of ANN models were analyzed by other mathematical models built by MATLAB programming. |
| format | Article |
| id | doaj-art-a5c1cbcc26f848448a3540cf41cc946a |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-a5c1cbcc26f848448a3540cf41cc946a2025-02-03T01:20:35ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/4857013Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural RoadsMohammad Habibzadeh0Mahmoud Ameri1Hassan Ziari2Neda Kamboozia3Seyede Mojde Sadat Haghighi4School of Civil EngineeringSchool of Civil EngineeringSchool of Civil EngineeringSchool of Civil EngineeringSchool of Civil EngineeringThe aim of the current research was to develop models to predict the severity of accidents on rural roads in Tehran province, Iran. In this regard, using accident data from 2017 to 2020, the machine learning algorithms, including multiple logistic regression, multilayer perceptron neural network (MLPNN), and radial basis function neural network (RBFNN) models, as well as statistical methods, including Kolmogorov–Smirnov test, Friedman test, and factor analysis, were implemented to determine the contributory factors in the severity of accidents. Thus, nine variables affecting the severity of accidents were considered in modeling, and then the effect of each variable was calculated. By comparing the results of artificial neural network (ANN) models and the Friedman test, it was indicated that the human factor had a remarkable effect on accident severity. In addition, both machine learning and statistical methods can be served as guidance for safety authorities to provide safety solutions, thereby leading to reducing accidents. Finally, the performances of ANN models were analyzed by other mathematical models built by MATLAB programming.http://dx.doi.org/10.1155/2022/4857013 |
| spellingShingle | Mohammad Habibzadeh Mahmoud Ameri Hassan Ziari Neda Kamboozia Seyede Mojde Sadat Haghighi Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads Journal of Advanced Transportation |
| title | Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads |
| title_full | Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads |
| title_fullStr | Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads |
| title_full_unstemmed | Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads |
| title_short | Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads |
| title_sort | presentation of machine learning approaches for predicting the severity of accidents to propose the safety solutions on rural roads |
| url | http://dx.doi.org/10.1155/2022/4857013 |
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