Prediction of Vehicular Traffic Flow Using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System
In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a Levenberg-Marquardt (LM) artificial neural network heuristic model was used to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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University of Žilina
2022-04-01
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| Series: | Communications |
| Subjects: | |
| Online Access: | https://komunikacie.uniza.sk/artkey/csl-202202-0001_prediction-of-vehicular-traffic-flow-using-levenberg-marquardt-artificial-neural-network-model-italy-road-tran.php |
| _version_ | 1797846735872065536 |
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| author | Isaac Oyeyemi Olayode Alessandro Severino Tiziana Campisi Lagouge Kwanda Tartibu |
| author_facet | Isaac Oyeyemi Olayode Alessandro Severino Tiziana Campisi Lagouge Kwanda Tartibu |
| author_sort | Isaac Oyeyemi Olayode |
| collection | DOAJ |
| description | In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a Levenberg-Marquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists. |
| first_indexed | 2024-04-09T18:00:58Z |
| format | Article |
| id | doaj.art-214b16ecd4f3469abd1bb7b2ffdceed0 |
| institution | Directory Open Access Journal |
| issn | 1335-4205 2585-7878 |
| language | English |
| last_indexed | 2024-04-09T18:00:58Z |
| publishDate | 2022-04-01 |
| publisher | University of Žilina |
| record_format | Article |
| series | Communications |
| spelling | doaj.art-214b16ecd4f3469abd1bb7b2ffdceed02023-04-14T06:32:23ZengUniversity of ŽilinaCommunications1335-42052585-78782022-04-01242E74E8610.26552/com.C.2022.2.E74-E86csl-202202-0001Prediction of Vehicular Traffic Flow Using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation SystemIsaac Oyeyemi Olayode0Alessandro Severino1Tiziana Campisi2Lagouge Kwanda Tartibu3Mechanical and Industrial Engineering Technology, University of Johannesburg, Johannesburg, South AfricaFaculty of Civil Engineering and Architecture, University of Catania, Catania, ItalyFaculty of Engineering and Architecture, Kore University of Enna, ItalyMechanical and Industrial Engineering Technology, University of Johannesburg, Johannesburg, South AfricaIn the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a Levenberg-Marquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.https://komunikacie.uniza.sk/artkey/csl-202202-0001_prediction-of-vehicular-traffic-flow-using-levenberg-marquardt-artificial-neural-network-model-italy-road-tran.phptraffic flowtraffic congestionlevenberg-marquardt artificial neural network modelartificial intelligenceitaly transportation system |
| spellingShingle | Isaac Oyeyemi Olayode Alessandro Severino Tiziana Campisi Lagouge Kwanda Tartibu Prediction of Vehicular Traffic Flow Using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System Communications traffic flow traffic congestion levenberg-marquardt artificial neural network model artificial intelligence italy transportation system |
| title | Prediction of Vehicular Traffic Flow Using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System |
| title_full | Prediction of Vehicular Traffic Flow Using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System |
| title_fullStr | Prediction of Vehicular Traffic Flow Using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System |
| title_full_unstemmed | Prediction of Vehicular Traffic Flow Using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System |
| title_short | Prediction of Vehicular Traffic Flow Using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System |
| title_sort | prediction of vehicular traffic flow using levenberg marquardt artificial neural network model italy road transportation system |
| topic | traffic flow traffic congestion levenberg-marquardt artificial neural network model artificial intelligence italy transportation system |
| url | https://komunikacie.uniza.sk/artkey/csl-202202-0001_prediction-of-vehicular-traffic-flow-using-levenberg-marquardt-artificial-neural-network-model-italy-road-tran.php |
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