Modeling hesitancy in airport choice: A comparison of discrete choice and machine learning methods

Abstract

Noticing that people may become more hesitant about their airport choice when exposed to abundant information and multi-channel on-line sales opportunities, this paper analyzes hesitancy in the choice of transfer airport in a multi-airport region using three approaches: a multinomial logit model, a random forests algorithm, and deep reinforcement learning. These methods are applied to the outcomes of a stated choice experiment, which was designed to survey the iterative airport choice process under hesitancy. The results indicate that a quarter of the passengers show evidence of hesitancy. Further, the deep reinforcement learning approach is found to provide the highest accuracy in representing the stated choices; the random forests algorithm also gives good results in capturing the reconsideration decision, while the multinomial logit model seems less accurate in representing the choice behavior of interest. Moreover, hesitancy involved a switch from flight-related attributes to ground transport-related attributes. Managerial implications for airlines and airports are discussed.