{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:02:29Z","timestamp":1790618549395,"version":"4.1.0"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52172330"],"award-info":[{"award-number":["52172330"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2022ZD0115600"],"award-info":[{"award-number":["2022ZD0115600"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    With the rapid expansion of unmanned aerial vehicle (UAV) applications in urban logistics, inspection, and emergency services, structured low-altitude networks are increasingly needed to support strategic airspace management. This paper proposes a computational fluid dynamics (CFD)-inspired method for identifying urban low-altitude traffic corridors (ULATCs). Rather than predicting atmospheric wind or actual UAV traffic dynamics, the method uses PhiFlow to construct synthetic incompressible velocity fields constrained by buildings and restricted areas. Multidirectional streamline tracing and geometric post-processing are then used to extract smooth, obstacle-avoiding candidate corridor centerlines. In a\n                    <jats:inline-formula id=\"pone.0357628.e001\">\n                      <jats:alternatives>\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" id=\"pone.0357628.e001g\" mimetype=\"image\" position=\"anchor\" xlink:href=\"info:doi\/10.1371\/journal.pone.0357628.e001\" xlink:type=\"simple\"\/>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" id=\"M1\">\n                          <mml:mrow>\n                            <mml:mn>5<\/mml:mn>\n                            <mml:mo>\u00d7<\/mml:mo>\n                            <mml:mn>5<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    km\n                    <jats:sup>2<\/jats:sup>\n                    case study in Shenzhen, China, the method identified 10 principal corridors with a mean individual length of 4,904 m and a unique network length of 40.271 km. Compared with the Voronoi\u2013A* and Risk-GNG baseline methods, CFD-FHVCT achieved a 500-m coverage ratio of 93.55% with the shortest unique network length and the highest length-weighted straightness. Path-planning results for six origin\u2013destination pairs further showed that the corridor-constrained paths had 42.3% lower total curvature, despite a 14.6% increase in path length, while mean flight time decreased by 2.2%. These results indicate that the identified network can support smoother trajectories with lower maneuvering demand. Overall, CFD-FHVCT provides a feasible computational framework for strategic urban low-altitude corridor identification and network design, with the synthetic velocity field used to guide corridor extraction rather than to predict physical wind or actual UAV traffic dynamics.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0357628","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:45:12Z","timestamp":1790617512000},"page":"e0357628","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying urban low-altitude traffic corridors: A computational fluid dynamics simulation approach"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5371-3134","authenticated-orcid":true,"given":"Zhipeng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Traffic and Transportation Engineering, Xinjiang 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