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Auton. Adapt. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>The Intelligent Transportation System (ITS) serves as a pivotal element within urban networks, offering decision support to users and connected automated vehicles through comprehensive information gathering, sensing, device control, and data processing. Presently, ITS predominantly relies on sensors embedded in fixed infrastructure, notably Roadside Units (RSUs). However, RSUs are confined by coverage limitations and may encounter challenges in prompt emergency responses. On-demand resources, such as drones, present a viable option to supplement these deficiencies effectively. This article introduces an approach where Software-Defined Networking and Mobile Edge Computing technologies are integrated to formulate a high-availability drone swarm control and communication infrastructure framework comprising the cloud layer, edge layer, and device layer. Drones confront limitations in flight duration attributed to battery limitations, posing a challenge in sustaining continuous monitoring of road conditions over extended periods. Effective drone scheduling stands as a promising solution to overcome these constraints. To tackle this issue, we initially utilized Graph WaveNet, a specialized graph neural network structure tailored for spatial-temporal graph modeling, for training a congestion prediction model using real-world dataset inputs. Building upon this, we further propose an algorithm for drone scheduling based on congestion prediction. Our simulation experiments using real-world data demonstrate that, compared to the baseline method, the proposed scheduling algorithm not only yielded superior scheduling gains but also mitigated drone idle rates.<\/jats:p>","DOI":"10.1145\/3673905","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T07:24:24Z","timestamp":1718781864000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Scheduling of High-Availability Drone Swarms for Congestion Alleviation in Connected Automated Vehicles"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5510-2867","authenticated-orcid":false,"given":"Shengye","family":"Pang","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7841-1253","authenticated-orcid":false,"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University, Ningbo, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1756-6102","authenticated-orcid":false,"given":"Zhen","family":"Qin","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1115-5652","authenticated-orcid":false,"given":"Xinkui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Ningbo, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9099-3792","authenticated-orcid":false,"given":"Jintao","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0953-6923","authenticated-orcid":false,"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4703-7348","authenticated-orcid":false,"given":"Jianwei","family":"Yin","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,13]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3129913"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3020220"},{"key":"e_1_3_1_4_2","first-page":"1","article-title":"Introduction to intelligent transportation systems","author":"Alam Muhammad","year":"2016","unstructured":"Muhammad Alam, Joaquim Ferreira, and Jos\u00e9 Fonseca. 2016. Introduction to intelligent transportation systems. In Intelligent Transportation Systems: Dependable Vehicular Communications for Improved Road Safety. 1\u201317.","journal-title":"Intelligent Transportation Systems: Dependable Vehicular Communications for Improved Road Safety."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2023.103204"},{"key":"e_1_3_1_6_2","first-page":"8","volume-title":"Proceedings of the 96th Annual Meeting of the Transportation Research Board","author":"Choi Youngmin","year":"2017","unstructured":"Youngmin Choi and Paul M. Schonfeld. 2017. Optimization of multi-package drone deliveries considering battery capacity. 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