This paper presents a novel methodology to develop itinerary choice models (ICM) for air travelers that addresses the limitations of the traditional utility-maximization approach. The methodology integrates a reinforcement learning algorithm and an airline network competition analysis model. The reinforcement learning algorithm searches for the values of parameters of the itinerary choice model while considering maximizing a reward function. The reward function is measured as the negative of the difference between the estimated and observed system metrics. The airline network competition analysis model is used to calculate the estimated system metrics. It is a simulation model that represents passenger-itinerary assignment. It captures the demand–supply interactions at the network level while considering the competition among all airlines. An ICM system is calibrated using the developed framework considering the global airline network, which includes more than 500,000 airport pairs. Validating the model against ground truth data shows that the developed model adequately captures the travelers’ itinerary choice behavior and replicates the competition pattern among airlines.

本文提出了一种新颖的方法来开发航空旅客的行程选择模型（ICM），解决了传统的效用最大化方法的局限性。该方法集成了强化学习算法和航空公司网络竞争分析模型。强化学习算法在考虑使奖励函数最大化的同时搜索路线选择模型的参数值。奖励函数被测量为估计的系统指标与观察到的系统指标之间的差异的负值。航空公司网络竞争分析模型用于计算估计的系统指标。这是一个模拟模型，代表了乘客-行程分配。它在考虑所有航空公司之间的竞争的同时，在网络级别捕获了供需交互。考虑到包括超过500,000个机场对的全球航空公司网络，使用已开发的框架对ICM系统进行了校准。根据地面真实数据对模型进行验证表明，所开发的模型能够充分捕获旅行者的行程选择行为，并复制航空公司之间的竞争模式。