Which Moments Matter? Heuristics of Remembered Travel Experience in Public Transport

2026-05-15

AI Summary

This study investigates how passengers integrate momentary experiences during transit into an overall satisfaction judgment to enhance public transportation experience and promote sustainable travel. Grounded in theories of experienced utility and remembered utility, the research employs smartphone-based experience sampling to collect 2,576 real-world trips and uses multilevel regression models to compare the predictive power of various temporal aggregation heuristics—including mean, peak-end, and a novel low-end rule—on post-trip satisfaction. For the first time in a real-world public transport context, the study validates and proposes the low-end heuristic as the optimal model, demonstrating that remembered evaluations are predominantly shaped by the most negative moment and the experience at trip’s end, significantly outperforming conventional models. The findings underscore that improving critical negative moments and terminal experiences can effectively elevate passengers’ remembered satisfaction.

Abstract

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.

Problem

• remembered utility
• travel satisfaction
• temporal aggregation
• public transport
• experience sampling

Innovation

• Minimum-End heuristic
• experience sampling
• remembered utility
• travel satisfaction
• temporal aggregation