Understanding how travelers form overall evaluations of public transport journeys is critical for improving travel satisfaction and encouraging sustainable mode choice. While travel satisfaction is discussed to influence attitudes and future behavior, the cognitive rules by which moment-to-moment experiences are aggregated into retrospective evaluations remain poorly understood in transport research. Drawing on psychological theories of experienced and remembered utility, this study investigates which temporal aggregation heuristics best predict post-trip travel satisfaction. Using a smartphone-based experience sampling approach, we collected high-frequency on-trip experience ratings and post-trip evaluations for 2576 real-world public transport trips across three German cities. Travel experience was assessed every five minutes during trips using a multi-item scale, allowing direct comparison of competing aggregation rules, including mean experience, peak-end, minimum-end, final moment, and trip duration. Multilevel regression models were estimated to evaluate the explanatory power of each heuristic. Results show that retrospective travel satisfaction is best predicted by a Minimum-End heuristic, combining the most negative moment of the journey and the final experience. Models based on mean experience, peak-end rules, final moment alone, or trip duration performed substantially worse. This pattern indicates that both negative extremes and the final phase of a journey independently contribute to remembered evaluations, rather than overall satisfaction reflecting an average of momentary experiences. The results have important implications for theory and practice, suggesting that targeted interventions at critical negative moments and at trip endings may yield substantial improvements in remembered satisfaction and, ultimately, support shifts toward sustainable mobility.

翻译：理解出行者如何形成对公共交通旅程的整体评价，对于提升出行满意度并促进可持续出行方式选择至关重要。尽管出行满意度被认为会影响态度和未来行为，但在交通研究中，人们对于时刻体验如何通过认知规则聚合为回顾性评价的机制仍知之甚少。本研究基于体验效用与记忆效用的心理学理论，探讨哪些时间聚合启发式规则最能预测行程后的出行满意度。通过智能手机体验抽样方法，我们收集了德国三个城市2576次真实公共交通出行的高频在途体验评分与行程后评价数据。每次行程期间每五分钟使用多维度量表评估出行体验，从而能够直接比较多种竞争性聚合规则，包括平均体验、峰终效应、最低点-终点效应、最终时刻和行程时长。通过估计多层回归模型，我们评估了每种启发式规则的解释力。结果显示，回顾性出行满意度最佳预测规则为“最低点-终点”启发式，该规则结合了旅程中最消极的时刻与最终体验。基于平均体验、峰终规则、单独最终时刻或行程时长的模型表现显著较差。这一模式表明，负面极端体验和旅程的最终阶段分别独立贡献于记忆评价，而非总体满意度反映瞬时体验的平均值。该结果对理论与实践具有重要意义，表明针对关键消极时刻和行程结束阶段的干预措施可能显著提升记忆满意度，并最终推动向可持续出行方式的转变。