Shifting travel from private cars to public transport is critical for meeting climate and related mobility goals, yet passengers will only choose transit if it offers a consistently positive experience. Previous studies of passenger satisfaction have largely relied on retrospective surveys, which overlook the dynamic and spatially differentiated nature of travel experience. This paper introduces a novel combination of real-time experience sampling and spatial hot spot analysis to capture and map where public transport users report consistently positive or negative experiences. Data were collected from 239 participants in Hamburg between March and September 2025. Using a smartphone application, travelers reported their momentary journey experience every five minutes during everyday trips, yielding over 21,000 in-situ evaluations. These geo-referenced data were analyzed with the Getis-Ord $Gi^{*}$ statistic to detect significant clusters of positive and negative travel experience. The analysis identified distinct hot and cold spots of travel experience across the network. Cold spots were shaped by heterogeneous problems, ranging from predominantly delay-dominated to overcrowding or socially stressful locations. In contrast, hot spots emerged through different pathways, including comfort-oriented, time-efficient or context-driven environments. The findings highlight three contributions. First, cold spots are not uniform but reflect specific local constellations of problems, requiring targeted interventions. Second, hot spots illustrate multiple success models that can serve as benchmarks for replication. Third, this study demonstrates the value of combining dynamic high-resolution sampling with spatial statistics to guide more effective and place-specific improvements in public transport.

翻译：将出行方式从私家车转向公共交通对于实现气候及相关出行目标至关重要，但乘客只有在获得持续积极的体验时才会选择公共交通。以往关于乘客满意度的研究大多依赖于回顾性调查，忽视了出行体验的动态性和空间差异化特征。本文引入了一种结合实时体验采样与空间热点分析的新方法，以捕捉并绘制公共交通用户报告持续积极或消极体验的位置。数据收集于2025年3月至9月期间，来自汉堡的239名参与者。通过智能手机应用，旅行者每五分钟报告一次其日常出行中的即时体验，共获得超过21,000条现场评估。这些地理参考数据使用Getis-Ord $Gi^{*}$ 统计量进行分析，以检测积极与消极出行体验的显著聚类。分析识别出了网络范围内出行体验的显著热点和冷点区域。冷点由异质性问题塑造，范围从以延误为主到过度拥挤或社交压力大的地点。相反，热点通过不同路径形成，包括以舒适为导向、时间高效或情境驱动的环境。研究结果凸显了三点贡献。第一，冷点并非同质，而是反映了特定的局部问题组合，需要有针对性的干预措施。第二，热点了多种成功模式，可作为复制的基准。第三，本研究展示了将动态高分辨率采样与空间统计相结合的价值，以指导公共交通中更有效和因地制宜的改进。