Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process
Short-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to p...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/3334810 |
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| _version_ | 1832566434213920768 |
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| author | Arash Rasaizadi Seyedehsan Seyedabrishami Mohammad Saniee Abadeh |
| author_facet | Arash Rasaizadi Seyedehsan Seyedabrishami Mohammad Saniee Abadeh |
| author_sort | Arash Rasaizadi |
| collection | DOAJ |
| description | Short-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to predict traffic state, categorized into A to C for segments of a rural road network. Since the temporal variation of rural road traffic is irregular, the performance of applied algorithms varies among different time intervals. To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. The Karaj-Chalus rural road traffic data was used as a case study to show how to implement it. As there are many influential features on traffic state, the genetic algorithm (GA) has been used to identify 25 of 32 features, which are the most influential on models’ fitness. Results show that the OL model as an ensemble learning model outperforms machine learning models, and its accuracy is equal to 80.03 percent. The highest balanced accuracy achieved by OL for predicting traffic states A, B, and C is 89, 73.4, and 58.5 percent, respectively. |
| format | Article |
| id | doaj-art-b8a2fc57549e48578d744a3bc48e8f79 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-b8a2fc57549e48578d744a3bc48e8f792025-02-03T01:04:11ZengWileyJournal of Advanced Transportation2042-31952021-01-01202110.1155/2021/3334810Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning ProcessArash Rasaizadi0Seyedehsan Seyedabrishami1Mohammad Saniee Abadeh2School of Civil and Environmental EngineeringSchool of Civil and Environmental EngineeringSchool of Electrical & Computer EngineeringShort-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to predict traffic state, categorized into A to C for segments of a rural road network. Since the temporal variation of rural road traffic is irregular, the performance of applied algorithms varies among different time intervals. To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. The Karaj-Chalus rural road traffic data was used as a case study to show how to implement it. As there are many influential features on traffic state, the genetic algorithm (GA) has been used to identify 25 of 32 features, which are the most influential on models’ fitness. Results show that the OL model as an ensemble learning model outperforms machine learning models, and its accuracy is equal to 80.03 percent. The highest balanced accuracy achieved by OL for predicting traffic states A, B, and C is 89, 73.4, and 58.5 percent, respectively.http://dx.doi.org/10.1155/2021/3334810 |
| spellingShingle | Arash Rasaizadi Seyedehsan Seyedabrishami Mohammad Saniee Abadeh Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process Journal of Advanced Transportation |
| title | Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process |
| title_full | Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process |
| title_fullStr | Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process |
| title_full_unstemmed | Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process |
| title_short | Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process |
| title_sort | short term prediction of traffic state for a rural road applying ensemble learning process |
| url | http://dx.doi.org/10.1155/2021/3334810 |
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