This study’s goal is to reduce the number of flights and alleviate congestion in hub airports. It proposes a flight time and frequency-optimization method for multiairport systems. A flight time and frequency-optimization model for multiairport system operation is created to minimize loss of passenger trip time. A k-means clustering algorithm is designed to solve the model and calculate indexes such as flight time and frequency, passenger trip-time loss, and distribution of airplane models and quantity. The calculation results of an example in China are as follows. Under multiairport system operation mode, passenger demands are divided into 7 categories; 11 flights satisfy all passenger demands; passenger trip-time loss is 129,573 min; and the average passenger load factor is 90.1%. Under an independent operation mode, passenger demands are divided into 8 categories; 13 flights satisfy all passenger demands; passenger trip-time loss is 173,705 min; and the average passenger load factor is 87.4%. The multiairport system operation mode not only improves passenger trip efficiency but also benefits airlines by improving the passenger load factor and reducing flights. Moreover, comparative analysis of a genetic algorithm versus a clustering algorithm further proves the accuracy of the clustering algorithm.
Improving urban bus emission and fuel consumption modeling by incorporating passenger load factor for real world driving