Cycling Speeds in Urban Traffic
This study investigates how cycling speeds vary across infrastructure types using open data from Hamburg, Germany, collected between 2022 and 2024. By integrating bicycle network data, tracking app-based cycling speeds, land use, and topographic information, key determinants of cycling speed are ide...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Findings Press
2025-07-01
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| Series: | Findings |
| Online Access: | https://doi.org/10.32866/001c.141204 |
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| _version_ | 1849718469628526592 |
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| author | Lucas Schuhmacher Gabriel Wilkes Martin Kagerbauer Peter Vortisch |
| author_facet | Lucas Schuhmacher Gabriel Wilkes Martin Kagerbauer Peter Vortisch |
| author_sort | Lucas Schuhmacher |
| collection | DOAJ |
| description | This study investigates how cycling speeds vary across infrastructure types using open data from Hamburg, Germany, collected between 2022 and 2024. By integrating bicycle network data, tracking app-based cycling speeds, land use, and topographic information, key determinants of cycling speed are identified through a gamma regression model. Results show that infrastructure type, surface conditions, and surrounding land use significantly affect speed. Dedicated cycling infrastructure promotes faster and more consistent speeds, especially on bituminous surfaces. Also, longer segments increase speeds. Urban or industrial areas tend to reduce speeds, while fields and forests lead to faster cycling speeds. |
| format | Article |
| id | doaj-art-4f6843c9831745cb8cdbf31c084c429e |
| institution | DOAJ |
| issn | 2652-8800 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Findings Press |
| record_format | Article |
| series | Findings |
| spelling | doaj-art-4f6843c9831745cb8cdbf31c084c429e2025-08-20T03:12:22ZengFindings PressFindings2652-88002025-07-0110.32866/001c.141204Cycling Speeds in Urban TrafficLucas SchuhmacherGabriel WilkesMartin KagerbauerPeter VortischThis study investigates how cycling speeds vary across infrastructure types using open data from Hamburg, Germany, collected between 2022 and 2024. By integrating bicycle network data, tracking app-based cycling speeds, land use, and topographic information, key determinants of cycling speed are identified through a gamma regression model. Results show that infrastructure type, surface conditions, and surrounding land use significantly affect speed. Dedicated cycling infrastructure promotes faster and more consistent speeds, especially on bituminous surfaces. Also, longer segments increase speeds. Urban or industrial areas tend to reduce speeds, while fields and forests lead to faster cycling speeds.https://doi.org/10.32866/001c.141204 |
| spellingShingle | Lucas Schuhmacher Gabriel Wilkes Martin Kagerbauer Peter Vortisch Cycling Speeds in Urban Traffic Findings |
| title | Cycling Speeds in Urban Traffic |
| title_full | Cycling Speeds in Urban Traffic |
| title_fullStr | Cycling Speeds in Urban Traffic |
| title_full_unstemmed | Cycling Speeds in Urban Traffic |
| title_short | Cycling Speeds in Urban Traffic |
| title_sort | cycling speeds in urban traffic |
| url | https://doi.org/10.32866/001c.141204 |
| work_keys_str_mv | AT lucasschuhmacher cyclingspeedsinurbantraffic AT gabrielwilkes cyclingspeedsinurbantraffic AT martinkagerbauer cyclingspeedsinurbantraffic AT petervortisch cyclingspeedsinurbantraffic |