Identifying urban low-altitude traffic corridors: A computational fluid dynamics simulation approach
- 1School of Traffic and Transportation Engineering, Xinjiang University, Urumqi, China
- 2College of Transportation, Tongji University, Shanghai, China
- 3School of Mechanical Engineering, Xinjiang University, Urumqi, China
Abstract
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 5×5 km2 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–A* 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–destination 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.
Related
- Tensor-Train Weak SINDy: Identifying High-Dimensional Nonlinear Dynamics
- Identifying Neural Dynamics Using Nonlinear Interventional State Space Models
- Computational fluid dynamics assessment of upper airway morphology and flow dynamics in infants with Robin sequence treated with the Stanford Orthodontic Airway Plate
- On the computational cost of Stochastic Gradient Langevin Dynamics
- Cosm: orchestrated collective dynamics give rise to rapid computational search at the solution frontier
BibTeX
@article{liu2026identifying,
title = {{Identifying urban low-altitude traffic corridors: A computational fluid dynamics simulation approach}},
author = {Zhipeng Liu and Zhizhou Wu and Jizhao Wang and Bin Pan and Wenxin Jiang},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357628},
url = {https://doi.org/10.1371/journal.pone.0357628},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identifying urban low-altitude traffic corridors: A computational fluid dynamics simulation approach
AU - Zhipeng Liu
AU - Zhizhou Wu
AU - Jizhao Wang
AU - Bin Pan
AU - Wenxin Jiang
PY - 2026
DA - 2026/09/28
JO - PLOS One
DO - 10.1371/journal.pone.0357628
UR - https://doi.org/10.1371/journal.pone.0357628
AB - 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 5×5 km2 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–A* 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–destination 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.
SN - 1932-6203
ER -