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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.21763 |
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| _version_ | 1866918403419144192 |
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| author | Bosch, Esther Scholz, Michael Sauerländer-Biebl, Anke Ihme, Klas |
| author_facet | Bosch, Esther Scholz, Michael Sauerländer-Biebl, Anke Ihme, Klas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21763 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mapping Travel Experience in Public Transport: Real-Time Evidence and Spatial Analysis in Hamburg Bosch, Esther Scholz, Michael Sauerländer-Biebl, Anke Ihme, Klas Human-Computer Interaction 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. |
| title | Mapping Travel Experience in Public Transport: Real-Time Evidence and Spatial Analysis in Hamburg |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.21763 |