公共交通旅行体验映射：汉堡的实时证据与空间分析
Mapping Travel Experience in Public Transport: Real-Time Evidence and Spatial Analysis in Hamburg
AI总结：
本文通过实时体验采样与空间热点分析，揭示了公共交通用户在汉堡的不同旅行体验区域，发现冷热斑点的成因及改进策略。
摘要：
将出行从私人汽车转向公共交通对于实现气候和相关移动目标至关重要，但乘客只有在公共交通提供一致积极体验时才会选择。以往的乘客满意度研究主要依赖回顾性调查，忽视了旅行体验的动态性和空间差异性。本文介绍了一种新的实时体验采样与空间热点分析相结合的方法，以捕捉和绘制公共交通用户报告的一致积极或消极体验的地点。数据来自2025年3月至9月期间在汉堡的239名参与者，使用智能手机应用程序，旅行者在日常出行中每五分钟报告一次即时旅程体验，获得超过21,000次现场评估。这些地理参考数据通过Getis-Ord $Gi^{*}$统计量分析，以检测积极和消极旅行体验的显著聚类。分析确定了网络中不同的热点和冷点区域。冷点由异质性问题构成，从以延误为主到拥挤或社交压力的地点。相比之下，热点通过不同的路径出现，包括以舒适为导向、时间高效或情境驱动的环境。研究结果突显了三个贡献：首先，冷点并非统一，而是反映特定地方问题的局部构成，需要有针对性的干预。其次，热点展示了多种成功模式，可以作为复制的基准。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，以指导更有效和地方特定的公共交通改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
