Utilizing Different Machine Learning Techniques to Examine Speeding Violations
This study investigated the potential impacts on speeding violations in the United States, including the top ten states in terms of crashes: California, Florida, Georgia, Illinois, Michigan, North Carolina, Ohio, Pennsylvania, Tennessee, and Texas. Several variables connected to the driver, surround...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/8/5113 |
| _version_ | 1797606493273456640 |
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| author | Ahmad H. Alomari Bara’ W. Al-Mistarehi Tasneem K. Alnaasan Motasem S. Obeidat |
| author_facet | Ahmad H. Alomari Bara’ W. Al-Mistarehi Tasneem K. Alnaasan Motasem S. Obeidat |
| author_sort | Ahmad H. Alomari |
| collection | DOAJ |
| description | This study investigated the potential impacts on speeding violations in the United States, including the top ten states in terms of crashes: California, Florida, Georgia, Illinois, Michigan, North Carolina, Ohio, Pennsylvania, Tennessee, and Texas. Several variables connected to the driver, surroundings, vehicle, road, and weather were investigated. Three different machine learning algorithms—Random Forest (RF), Classification and Regression Tree (CART), and Multi-Layer Perceptron (MLP)—were applied to predict speeding violations. Accuracy, F-measure, Kappa statistic, Root Mean Squared Error (RMSE), Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) were used to evaluate the algorithms’ performance. Findings showed that age, accident year, road alignment, weather, accident time, and speed limits are the most significant variables. The algorithms used showed excellent ability in analyzing and predicting speeding violations. The RF was the best method for analyzing and predicting speeding violations. Understanding how these factors affect speeding violations helps decision-makers devise ways to cut down on these violations and make the roads safer. |
| first_indexed | 2024-03-11T05:15:58Z |
| format | Article |
| id | doaj.art-64300ad4f1a048bab0952315e7ea30d6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:15:58Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-64300ad4f1a048bab0952315e7ea30d62023-11-17T18:13:50ZengMDPI AGApplied Sciences2076-34172023-04-01138511310.3390/app13085113Utilizing Different Machine Learning Techniques to Examine Speeding ViolationsAhmad H. Alomari0Bara’ W. Al-Mistarehi1Tasneem K. Alnaasan2Motasem S. Obeidat3Department of Civil Engineering, Yarmouk University (YU), P.O. Box 566, Irbid 21163, JordanDepartment of Civil Engineering, Jordan University of Science & Technology (JUST), P.O. Box 3030, Irbid 22110, JordanDepartment of Civil Engineering, Jordan University of Science & Technology (JUST), P.O. Box 3030, Irbid 22110, JordanDepartment of Computer Science, Yarmouk University (YU), P.O. Box 566, Irbid 21163, JordanThis study investigated the potential impacts on speeding violations in the United States, including the top ten states in terms of crashes: California, Florida, Georgia, Illinois, Michigan, North Carolina, Ohio, Pennsylvania, Tennessee, and Texas. Several variables connected to the driver, surroundings, vehicle, road, and weather were investigated. Three different machine learning algorithms—Random Forest (RF), Classification and Regression Tree (CART), and Multi-Layer Perceptron (MLP)—were applied to predict speeding violations. Accuracy, F-measure, Kappa statistic, Root Mean Squared Error (RMSE), Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) were used to evaluate the algorithms’ performance. Findings showed that age, accident year, road alignment, weather, accident time, and speed limits are the most significant variables. The algorithms used showed excellent ability in analyzing and predicting speeding violations. The RF was the best method for analyzing and predicting speeding violations. Understanding how these factors affect speeding violations helps decision-makers devise ways to cut down on these violations and make the roads safer.https://www.mdpi.com/2076-3417/13/8/5113speeding violationsmachine learningClassification and Regression TreeRandom ForestMulti-Layer Perceptron |
| spellingShingle | Ahmad H. Alomari Bara’ W. Al-Mistarehi Tasneem K. Alnaasan Motasem S. Obeidat Utilizing Different Machine Learning Techniques to Examine Speeding Violations Applied Sciences speeding violations machine learning Classification and Regression Tree Random Forest Multi-Layer Perceptron |
| title | Utilizing Different Machine Learning Techniques to Examine Speeding Violations |
| title_full | Utilizing Different Machine Learning Techniques to Examine Speeding Violations |
| title_fullStr | Utilizing Different Machine Learning Techniques to Examine Speeding Violations |
| title_full_unstemmed | Utilizing Different Machine Learning Techniques to Examine Speeding Violations |
| title_short | Utilizing Different Machine Learning Techniques to Examine Speeding Violations |
| title_sort | utilizing different machine learning techniques to examine speeding violations |
| topic | speeding violations machine learning Classification and Regression Tree Random Forest Multi-Layer Perceptron |
| url | https://www.mdpi.com/2076-3417/13/8/5113 |
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