A Methodology for Increasing Convergence Speed of Traffic Assignment Algorithms Based on the Use of a Generalised Averaging Function
In this paper, we propose a generalisation of the Method of Successive Averages (MSA) for solving traffic assignment problems. The generalisation consists in proposing a different step sequence within the general MSA framework to reduce computing times. The proposed step sequence is based on the mod...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/16/5698 |
| _version_ | 1797557596534603776 |
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| author | Marilisa Botte Mariano Gallo Mario Marinelli Luca D’Acierno |
| author_facet | Marilisa Botte Mariano Gallo Mario Marinelli Luca D’Acierno |
| author_sort | Marilisa Botte |
| collection | DOAJ |
| description | In this paper, we propose a generalisation of the Method of Successive Averages (MSA) for solving traffic assignment problems. The generalisation consists in proposing a different step sequence within the general MSA framework to reduce computing times. The proposed step sequence is based on the modification of the classic 1/k sequence for improving the convergence speed of the algorithm. The reduction in computing times is useful if the assignment problems are subroutines of algorithms for solving Network Design Problems—such algorithms require estimation of the equilibrium traffic flows at each iteration, hence, many thousands of times for real-scale cases. The proposed algorithm is tested with different parameter values and compared with the classic MSA algorithm on a small and on two real-scale networks. A test inside a Network Design Problem is also reported. Numerical results show that the proposed algorithm outperforms the classic MSA with reductions in computing times, reaching up to 79%. Finally, the theoretical properties are studied for stating the convergence of the proposed algorithm. |
| first_indexed | 2024-03-10T17:19:05Z |
| format | Article |
| id | doaj.art-38165593569c4b2fa4c8a1d5ee775530 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T17:19:05Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-38165593569c4b2fa4c8a1d5ee7755302023-11-20T10:23:05ZengMDPI AGApplied Sciences2076-34172020-08-011016569810.3390/app10165698A Methodology for Increasing Convergence Speed of Traffic Assignment Algorithms Based on the Use of a Generalised Averaging FunctionMarilisa Botte0Mariano Gallo1Mario Marinelli2Luca D’Acierno3Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Via Claudio 21, 80125 Naples, ItalyDepartment of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, ItalyDepartment of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, ItalyDepartment of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Via Claudio 21, 80125 Naples, ItalyIn this paper, we propose a generalisation of the Method of Successive Averages (MSA) for solving traffic assignment problems. The generalisation consists in proposing a different step sequence within the general MSA framework to reduce computing times. The proposed step sequence is based on the modification of the classic 1/k sequence for improving the convergence speed of the algorithm. The reduction in computing times is useful if the assignment problems are subroutines of algorithms for solving Network Design Problems—such algorithms require estimation of the equilibrium traffic flows at each iteration, hence, many thousands of times for real-scale cases. The proposed algorithm is tested with different parameter values and compared with the classic MSA algorithm on a small and on two real-scale networks. A test inside a Network Design Problem is also reported. Numerical results show that the proposed algorithm outperforms the classic MSA with reductions in computing times, reaching up to 79%. Finally, the theoretical properties are studied for stating the convergence of the proposed algorithm.https://www.mdpi.com/2076-3417/10/16/5698traffic assignmentMSA algorithmfixed-point problemsnetwork design |
| spellingShingle | Marilisa Botte Mariano Gallo Mario Marinelli Luca D’Acierno A Methodology for Increasing Convergence Speed of Traffic Assignment Algorithms Based on the Use of a Generalised Averaging Function Applied Sciences traffic assignment MSA algorithm fixed-point problems network design |
| title | A Methodology for Increasing Convergence Speed of Traffic Assignment Algorithms Based on the Use of a Generalised Averaging Function |
| title_full | A Methodology for Increasing Convergence Speed of Traffic Assignment Algorithms Based on the Use of a Generalised Averaging Function |
| title_fullStr | A Methodology for Increasing Convergence Speed of Traffic Assignment Algorithms Based on the Use of a Generalised Averaging Function |
| title_full_unstemmed | A Methodology for Increasing Convergence Speed of Traffic Assignment Algorithms Based on the Use of a Generalised Averaging Function |
| title_short | A Methodology for Increasing Convergence Speed of Traffic Assignment Algorithms Based on the Use of a Generalised Averaging Function |
| title_sort | methodology for increasing convergence speed of traffic assignment algorithms based on the use of a generalised averaging function |
| topic | traffic assignment MSA algorithm fixed-point problems network design |
| url | https://www.mdpi.com/2076-3417/10/16/5698 |
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