This paper proposes a two-layered genetic algorithm for the design of a demand-oriented UAS public route network. Unlike traditional UAS route networks that rely solely on geographic data for ground obstacle avoidance, the proposed method leverages traffic demand information to coordinate the relation between airspace layout and traffic assignment. First, a bi-level programming model is established, where the route network topology is designed by the outer model, and the corresponding traffic assignment is determined by the inner model. Specifically, the Genetic Algorithm (GA) is adopted to solve the above programming model and a branch exchange strategy is utilized to reduce the blindness of the initial search. A case study based on the forecast UAS travel demand data of Shanghai City in China is conducted. Results demonstrate that the proposed method can improve UAS transportation efficiency and reduce network construction cost simultaneously.
Application of Two-Layered Genetic Algorithm for Demand-Oriented UAS Public Route Network Design
2024-09-29
815517 byte
Conference paper
Electronic Resource
English
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