Urban connected vehicle lane planning based on improved Frank Wolfe algorithm
As the new generation of information technology matures and improves, the functions of intelligent connected vehicles become more and more perfect, and the number of urban connected vehicles is also increasing. To provide an effective optimization scheme to the mixed traffic flow road network in the...
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| Format: | Article |
| Language: | en |
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Public Library of Science
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/123718/1/123718.pdf http://psasir.upm.edu.my/id/eprint/123718/ https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0321540 |
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| author | Jiang, Anqi Abdul Aziz, Faziawati Ujang, Norsidah Mohamed, Mohd Afzan |
| author_facet | Jiang, Anqi Abdul Aziz, Faziawati Ujang, Norsidah Mohamed, Mohd Afzan |
| author_sort | Jiang, Anqi |
| building | UPM Library |
| collection | Institutional Repository |
| content_provider | Universiti Putra Malaysia |
| content_source | UPM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | As the new generation of information technology matures and improves, the functions of intelligent connected vehicles become more and more perfect, and the number of urban connected vehicles is also increasing. To provide an effective optimization scheme to the mixed traffic flow road network in the networked environment, the study investigates the lane planning for urban connected vehicles method. First, a lane planning for urban connected vehicles bi-level programming model is constructed. Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. The proposed improved Frank Wolfe algorithm can converge at around 30 iterations, with a convergence limit of around 10-4, which is superior to the traditional Frank Wolfe algorithm. The minimum total travel cost of the road system gradually decreases with the increase of the fairness index threshold. The experimental results demonstrate the effectiveness of the proposed urban connected vehicle lane planning model and solving algorithm. The research results contribute to improving the operational safety and efficiency of the road network TS, thereby improving the current traffic situation of the urban TS. |
| format | Article |
| id | my.upm.eprints-123718 |
| institution | Universiti Putra Malaysia |
| language | en |
| publishDate | 2025 |
| publisher | Public Library of Science |
| record_format | eprints |
| spelling | my.upm.eprints-1237182026-03-17T06:30:59Z http://psasir.upm.edu.my/id/eprint/123718/ Urban connected vehicle lane planning based on improved Frank Wolfe algorithm Jiang, Anqi Abdul Aziz, Faziawati Ujang, Norsidah Mohamed, Mohd Afzan As the new generation of information technology matures and improves, the functions of intelligent connected vehicles become more and more perfect, and the number of urban connected vehicles is also increasing. To provide an effective optimization scheme to the mixed traffic flow road network in the networked environment, the study investigates the lane planning for urban connected vehicles method. First, a lane planning for urban connected vehicles bi-level programming model is constructed. Then, the upper-level model is solved using improved whale optimization, and the lower-level model is solved using improved Frank-Wolfe algorithm. The results showed that the accuracy and recall of the proposed improved whale optimization algorithm on the Iris dataset were 95.27% and 92.65%, respectively, which were superior to traditional whale optimization algorithm, moth flame optimization algorithm, moth flame optimization algorithm combined with chaos strategy, and adaptive firefly optimization algorithm. The proposed improved Frank Wolfe algorithm can converge at around 30 iterations, with a convergence limit of around 10-4, which is superior to the traditional Frank Wolfe algorithm. The minimum total travel cost of the road system gradually decreases with the increase of the fairness index threshold. The experimental results demonstrate the effectiveness of the proposed urban connected vehicle lane planning model and solving algorithm. The research results contribute to improving the operational safety and efficiency of the road network TS, thereby improving the current traffic situation of the urban TS. Public Library of Science 2025-04-22 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/123718/1/123718.pdf Jiang, Anqi and Abdul Aziz, Faziawati and Ujang, Norsidah and Mohamed, Mohd Afzan (2025) Urban connected vehicle lane planning based on improved Frank Wolfe algorithm. PLoS ONE, 20 (4). art. no. e0321540. pp. 1-20. ISSN 1932-6203 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0321540 Multidisciplinary 10.1371/journal.pone.0321540 |
| spellingShingle | Multidisciplinary Jiang, Anqi Abdul Aziz, Faziawati Ujang, Norsidah Mohamed, Mohd Afzan Urban connected vehicle lane planning based on improved Frank Wolfe algorithm |
| title | Urban connected vehicle lane planning based on improved Frank Wolfe algorithm |
| title_full | Urban connected vehicle lane planning based on improved Frank Wolfe algorithm |
| title_fullStr | Urban connected vehicle lane planning based on improved Frank Wolfe algorithm |
| title_full_unstemmed | Urban connected vehicle lane planning based on improved Frank Wolfe algorithm |
| title_short | Urban connected vehicle lane planning based on improved Frank Wolfe algorithm |
| title_sort | urban connected vehicle lane planning based on improved frank wolfe algorithm |
| topic | Multidisciplinary |
| url | http://psasir.upm.edu.my/id/eprint/123718/1/123718.pdf http://psasir.upm.edu.my/id/eprint/123718/ https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0321540 |
| url_provider | http://psasir.upm.edu.my/ |