Data-driven Optimization of Nonscheduled International Air Cargo Routes
In the context of intelligent civil aviation construction,considering the inefficiency and lack of systematization in traditional manual route planning,a mathematical model was established for route planning based on optimization theory,considering airport and route point restrictions.The traditional A*algorithm was improved through dynamic programming for intelligent route planning.Visual output of route planning and global historical route data was realized through data mining,cleaning,and coordinate point transformation.Using the Python simulation platform with 2022 global route data and 14,110 airports,the improved A*algorithm was compared with the classic greedy algorithm for long-haul and short-haul routes.The results show the improved A*algorithm reduces node count and route length,with a 37.66％reduction for long-haul routes and a 4.36％reduction for short-haul routes,enhancing planning efficiency and accuracy.
data-driveninternational air cargononscheduled route planningimproved A* algorithmroute visualization
万方数据
维普
