ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment
The behaviour of ants has inspired various scientific disciplines due to their ability to solve even complex problems. During their movement, ants generate trail networks that share many characteristics with vehicular traffic on highways. This research aims to estimate the values of traffic flow var...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Transportation Research Interdisciplinary Perspectives |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590198225000107 |
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| author | Marco Guerrieri Nicola Pugno |
| author_facet | Marco Guerrieri Nicola Pugno |
| author_sort | Marco Guerrieri |
| collection | DOAJ |
| description | The behaviour of ants has inspired various scientific disciplines due to their ability to solve even complex problems. During their movement, ants generate trail networks that share many characteristics with vehicular traffic on highways. This research aims to estimate the values of traffic flow variables (mean speed, density, and flow) in ant trails without intersections or branches that could alter the dynamics of each ant. A case study in an outdoor environment was analyzed. The macroscopic traffic flow variables of interest were estimated using the deep learning method and the YOLO detection algorithm. The results show that ants adopt specific traffic strategies (platoon formation, quasi-constant speed and no overtaking maneuvers) that help avoid jam phenomena, even at high density. Emerging technologies, including smart roads, communication systems, and Cooperative and Automated Vehicles (CAVs), allow us to speculate on the use of traffic control systems inspired by ant behaviour to avoid the risk of congestion even at high traffic volumes, as demonstrated by the preliminary results of this research. |
| format | Article |
| id | doaj-art-3430bbb1212a4724b845e935cf340e98 |
| institution | Kabale University |
| issn | 2590-1982 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transportation Research Interdisciplinary Perspectives |
| spelling | doaj-art-3430bbb1212a4724b845e935cf340e982025-02-09T05:01:19ZengElsevierTransportation Research Interdisciplinary Perspectives2590-19822025-01-0129101331ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environmentMarco Guerrieri0Nicola Pugno1Department of Civil, Environmental and Mechanical Engineering, University of Trento, via Mesiano 77, 38123 Trento, Italy; Corresponding author.Laboratory for Bioinspired, Bionic, Nano, Meta Materials & Mechanics, Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123 Trento, Italy; School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, E1 4NS London, United KingdomThe behaviour of ants has inspired various scientific disciplines due to their ability to solve even complex problems. During their movement, ants generate trail networks that share many characteristics with vehicular traffic on highways. This research aims to estimate the values of traffic flow variables (mean speed, density, and flow) in ant trails without intersections or branches that could alter the dynamics of each ant. A case study in an outdoor environment was analyzed. The macroscopic traffic flow variables of interest were estimated using the deep learning method and the YOLO detection algorithm. The results show that ants adopt specific traffic strategies (platoon formation, quasi-constant speed and no overtaking maneuvers) that help avoid jam phenomena, even at high density. Emerging technologies, including smart roads, communication systems, and Cooperative and Automated Vehicles (CAVs), allow us to speculate on the use of traffic control systems inspired by ant behaviour to avoid the risk of congestion even at high traffic volumes, as demonstrated by the preliminary results of this research.http://www.sciencedirect.com/science/article/pii/S2590198225000107Ant-Inspired TrafficDeep learningTrafficCongestionPlatoonsSmart roads |
| spellingShingle | Marco Guerrieri Nicola Pugno ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment Transportation Research Interdisciplinary Perspectives Ant-Inspired Traffic Deep learning Traffic Congestion Platoons Smart roads |
| title | ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment |
| title_full | ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment |
| title_fullStr | ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment |
| title_full_unstemmed | ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment |
| title_short | ANTi-JAM solutions for smart roads: Ant-inspired traffic flow rules under CAVs environment |
| title_sort | anti jam solutions for smart roads ant inspired traffic flow rules under cavs environment |
| topic | Ant-Inspired Traffic Deep learning Traffic Congestion Platoons Smart roads |
| url | http://www.sciencedirect.com/science/article/pii/S2590198225000107 |
| work_keys_str_mv | AT marcoguerrieri antijamsolutionsforsmartroadsantinspiredtrafficflowrulesundercavsenvironment AT nicolapugno antijamsolutionsforsmartroadsantinspiredtrafficflowrulesundercavsenvironment |