Taking learning into account when modelling passengers’ route choice behaviour improves understanding and forecasting of their preferences, which helps stakeholders better design public transport systems to meet user needs. Most empirical studies have neglected the relationship between current choices and passengers’ past experiences that lead to a learning process about route attributes. This study addresses this gap by using real observed choices from smart-card data to implement a route choice model that takes into account the learning process of passengers during the inauguration of a new metro line in Santiago, Chile. An instance-based learning (IBL) model is used to represent individually perceived in-vehicle travel time in the route choice model. It accounts for recency and reinforcement of experience using the power law of forgetting. The empirical evaluation uses 8 weeks of smart-card data after the introduction of the metro line. Model parameters are evaluated, and the fit and behavioural coherence achieved by the IBL route choice model is measured against a baseline model. The baseline model neglects passenger learning from experience and assumes that all passengers use only trip descriptive information in their decision-making process. The IBL route choice model outperforms the baseline model from the fourth week after the introduction of the metro line. This empirical evidence supports the notion that after the introduction of a new metro line, passengers initially rely on descriptive travel information to estimate travel times for new alternatives. After a few weeks, they begin to incorporate their own experiences to update their perceptions.

在对乘客的路线选择行为进行建模时考虑学习可以提高对其偏好的理解和预测，这有助于利益相关者更好地设计公共交通系统以满足用户需求。大多数实证研究都忽略了当前选择与乘客过去经历之间的关系，而这种关系导致了对路线属性的学习过程。本研究通过使用智能卡数据中真实观察到的选择来实现路线选择模型，该模型考虑了智利圣地亚哥新地铁线路开通期间乘客的学习过程，从而解决了这一差距。基于实例的学习（IBL）模型用于表示路线选择模型中个体感知的车内行程时间。它利用遗忘的幂律来解释新近性和经验的强化。实证评估使用了地铁线路开通后 8 周的智能卡数据。评估模型参数，并根据基线模型测量 IBL 路线选择模型实现的拟合度和行为一致性。基线模型忽略了乘客从经验中学习，并假设所有乘客在决策过程中仅使用行程描述性信息。从地铁线路开通后的第四周开始，IBL 路线选择模型的表现就优于基线模型。这一经验证据支持这样一种观点，即在引入新的地铁线路后，乘客最初依靠描述性出行信息来估计新替代方案的出行时间。几周后，他们开始结合自己的经历来更新自己的看法。