Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1016/j.tbs.2023.100694
Titel (primär) Estimating daily bicycle counts with Strava data in rural and urban locations
Autor Jean-Louis, Gilles; Eckhardt, M.; Podschun, S.; Mahnkopf, J.; Venohr, M.
Quelle Travel Behaviour and Society
Erscheinungsjahr 2024
Department OEKON
Band/Volume 34
Seite von art. 100694
Sprache englisch
Topic T5 Future Landscapes
Keywords Daily bicycle traffic; Bicycle counts; Spatial differences; Crowdsourced data; Strava; Generalised Boosted Regression Models
Abstract Reliable information on daily bicycle traffic provides a fundamental basis for city planners and scientists. To estimate daily bicycle counts for various German locations with different degrees of urbanisation, we applied Generalised Boosted Regression Models. Altogether 44,136 daily datapoints from 46 counter locations covering a time period of four years were considered. Crowdsourced fitness tracker data from Strava, socio-demographics, land use and weather data were used as independent variables. Our results indicate that weather has the strongest influence on estimated bicycle counts, exceeding the relevance of fitness tracker data. In an overall model daily bicycle counts were estimated with a mean absolute percentage error (MAPE) of 27.9 %. In terms of location-specific estimations, a MAPE of 11.2 % was reached. With our approach, high-quality out-of-sample predictions are also feasible. Based on our estimations, we assume the volatility of fitness tracker user share to have a major impact on model accuracy.
dauerhafte UFZ-Verlinkung
Jean-Louis, Gilles, Eckhardt, M., Podschun, S., Mahnkopf, J., Venohr, M. (2024):
Estimating daily bicycle counts with Strava data in rural and urban locations
Travel Behav. Soc. 34 , art. 100694 10.1016/j.tbs.2023.100694