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Citation |
von Stülpnagel R, Krukar J. Accid. Anal. Prev. 2018; 121: 109-117. |
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Affiliation |
Institute for Geoinformatics, Münster University, Heisenbergstraße 2, 48149. Münster, Germany. Electronic address: krukar@uni-muenster.de. |
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Copyright |
(Copyright © 2018, Elsevier Publishing) |
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DOI |
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PMID |
30243039 |
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Abstract |
Subjective risk perception during urban cycling has been mostly investigated through questionnaire studies. However, newly available data sources promise extended possibilities for the investigation and understanding of the underlying factors. We validate the rationale for using both opportunistically available crowd-sourced data (i.e., volunteered geographic information or VGI) as well as more established but rarely investigated authoritative data as predictors of subjective cycling risk. We achieve this by correlating indicators of cycling risk extracted from both VGI and authoritative data for two different German cities with participants' risk estimates assessed in laboratory-based virtual reality experiments. In Case 1, 15 participants (mostly undergraduate students with a mean age of 22 years old; nine of them females) were tested as a sample representing frequent and experienced cyclists, but unfamiliar with the 19 tested locations and less likely to be affected by the virtual reality setup. In Case 2, 24 new participants (mostly undergraduate students; mean age 24 years; 13 of them females) were experienced cyclists and mostly familiar with the 40 test locations located in their city of residence. For both cases, our findings provide evidence that parameters extracted from VGI (e.g., the semantic severity of the contribution and the reception by other citizens) as well as from authoritative data sources (e.g., accident statistics or Space Syntax measures) represent valid indicators for the subjectively perceived risk of cycling at a specific location. On the basis of this validation, future research can use these data sources to investigate the sources of risk perception during urban cycling in greater detail. Language: en |
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Keywords |
Accident statistics; Risk perception; Space syntax; Urban cycling; Volunteered geographic information |