Airline flight frequency optimization based on multiple travel paths

A multiple-layer network was abstracted from the airline's air transport network, and a two-stage planning model was built to determine the flight frequency along a certain route by an airline for a specific city pair. In the first stage, a negative utility function of travel was constructed on the basis of the passengers' selection behavior by considering their perception of travel time, transfer time, delay time, and ticket price. Subsequently, a polynomial Logit model was adopted to create a route selection model in order to calculate the probability of passengers selecting a certain route by an airline for a specific city pair. In the second stage, a linear planning model was established to determine the flight frequency from the airline's perspective. The overall objective was to maximize the total revenue, the multiple travel paths, the total carrier capacity of the airline, and the balance between the carrier supply and demand for each path were considered. An iterative algorithm was presented to solve the proposed two-stage model. Analysis result shows that the convergence can be achieved after 8 iterations, and thus, the optimal solution can be reached within a short time. As the solutions converge, the proposed two-stage planning model prioritizes the routes with the highest revenue to improve the overall revenue in cases where there is market competition and insufficient overall capacity. For the routes with multiple segments, the two-stage model can more clearly present the role of the passengers' selection behavior related to the flight frequency determination. For the routes with only one segment, there is less variation in demand with respect to the change in the flight frequency. As the number of iterations increases, the demand tends to become decreasingly sensitive to the flight frequency. For the routes with multiple segments, the variation in the demand with change in the flight frequency is considerably higher in the cases when the total demand is fixed. Conversely, the demand decreases sharply when the flight frequency remains unchanged due to the market competition. Therefore, the presented model and algorithm can effectively improve the airline revenue.