Planning bicycle infrastructure significantly depends on data that provide adequate information. Various studies indicate that GPS data, which have been collected via smartphone application by cyclists themselves, could provide that information. The article presents the results of a recently conducted study that evaluates the usability of such data for bicycle traffic planning in German cities. We used different methods (web-survey, focus group interview, data analysis) to investigate data needs of German municipal traffic planners and oppose it to the information deduced and computed from commercially available data provided by Strava Inc. The article reveals that the provided data are, in general, useful, but there are also serious limitations that must be considered.
bicycle traffic, GPS data, traffic planning, smartphone
 Kager, R. & Bertolini, L. & Te Brömmelstroet, M., Characterisation of and reﬂections on the synergy of bicycles and public transport. Transportation Research Part A: Policy and Practice, 85, pp. 208–219, 2016. DOI: 10.1016/j.tra.2016.01.015.
 Bicycle Traffic in Germany – Numbers, Data, Facts. Federal Ministry of Transport and Digital Infrastructure, available at http://www.ziv-zweirad.de/uploads/media/rad-verkehr-in-zahlen.pdf. 2014, (accessed 23 March 2018).
 Birk, M. & Geller, R., Bridging the gaps: how quality and quantity of a connected bikeway network correlates with increasing bicycle use. Transportation Research Board, 85th Annual Meeting, Washington, 2006.
 Lißner, S., Francke, A., Chernyshova, O. & Becker, T., App-Daten für die Radverkehrsplanung – Eine explorative Datenanalyse von GPS-Daten im Radverkehr. Internationales Verkehrswesen, 69(1), pp. 48–52, 2017.
 Hyde-Wright, A., Graham, B. & Nordback, K., Counting bicyclists with pneumatic tube counters on shared roadways. ITE Journal (Institute of Transportation Engineers), 84(2), pp. 32–37, ISSN 0162-8178, 2014.
 Jestico, B., Nelson, T. & Winters, M., Mapping ridership using crowdsourced cycling data. Journal of Transport Geography. 52, pp. 90–97, 2016. DOI: 10.1016/j. jtrangeo.2016.03.006.
 Statista, Anteil der Smartphone-Nutzer in Deutschland in den Jahren 2012 bis 2017, available at https://de.statista.com/statistik/daten/studie/585883/umfrage/anteil-der-smartphone-nutzer-in-deutschland/, 2018 (accessed 23 March 2018).
 Thornhill, T., Globally cyclists clocked up 4.5 BILLION miles and Regent’s Park is the UK’s most popular bike route: GPS training app reveals how much exercise the world did in 2017. Mail Online, 2017.
 Romanillos, G., Austwick, M.Z., Ettema, D. & De Kruijf, J., Big data and cycling. Transport Reviews, 36(1), pp. 114–133, 2015. DOI: 10.1371/journal.pone.0074685.
 Milne, D. & Watling, D., Big data and understanding change in the context of planning transport systems, Journal of Transport Geography, Article in Press, 2018. DOI: 10.1016/j.jtrangeo.2017.11.004.
 Clarke, A. & Steele, R., How personal fitness data can be re-used by smart cities. Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Adelaide, 2011.
 Wamsley, K., Optimal power-based cycling pacing strategies for Strava segments. Doctoral dissertation, University of Pennsylvania: Kutztown, 2014.
 Albergotti, R., Strava, popular with cyclists and runners, wants to sell its data to urban planners. Wall Street Journal, available at https://blogs.wsj.com/digits/2014/05/07/strava-popular-with-cyclists-and-runners-wants-to-sell-its-data-to-urban-planners/, 2014 (accessed 23 March 2018).
 Musakwa, W. & Selala, K.M., Mapping cycling patterns and trends using Strava Metro data in the city of Johannesburg, South Africa. Data in Brief, 9, pp. 898–905, 2016. DOI: 10.1016/j.dib.2016.11.002.
 Holmgren, J., Aspegren, S. & Dahlström, J., Prediction of bicycle counter data using regression. The 2nd Edition of the International Workshop on Data Mining on IoT Systems (DaMIS), 18–20 September, Lund, 2017.
 Heesch, K.C. & Langdon, M., The usefulness of GPS bicycle tracking data for evaluating the impact of infrastructure change on cycling behaviour. Health Promotion Journal of Australia: Official Journal of Australian Association of Health Promotion Professionals, 27 (3), pp. 222–229 2016. DOI: 10.1071/HE16032.
 Boss, D., Nelson, T., Winters, M. & Ferster, C.J., Using crowdsourced data to monitor change in spatial patterns of bicycle ridership. Journal of Transport Health. Article in Press, 9, pp. 226–233, 2018. DOI: 10.1016/j.jth.2018.02.008.
 Conrow, L., Wentz, E., Nelson, T. & Pettit, C., Comparing spatial patterns of crowdsourced and conventional bicycling datasets. Applied Geography, 92, 21–30, 2018. DOI: 10.1016/j.apgeog.2018.01.009.
 Richardson, A.J., Ampt, E.S. & Meyburg, A.H., Survey Methods for Transport Planning, available at http://www.no2hcf.co.uk/docs/Traffic_survey_form.pdf, 2006 (accessed 23 March 2018).
 Fleming, C.M. & Bowden, M., Web-based surveys as an alternative to traditional mail methods. Journal of Environmental Management, 90, 284–292, 2009. DOI: 10.1016/j. jenvman.2007.09.011.
 Wright, K.B., Researching internet-based populations: advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of Computer-Mediated Communication, 10(3), JCMC1034, 2005. DOI: 10.1111/j.1083-6101.2005.tb00259.x.
 Stage, F.K. & Manning, K., Research in the College Context: Approaches and Methods. Routledge, Abingdon-on-Thames, United Kingdom, 2015.
 Ledermann, L.C., Assessing educational effectiveness: the focus group interview as a technique for data collection. Communication Education, 39, pp. 117–127, 2009. DOI: 10.1080/03634529009378794.
 Ausserer, K., Kaufmann, C. & Risser, R., The Focus Group Interview – A qualitative method to assess quality aspects of the traffic systems. Proceeding of the 20th ICTCT Workshop, Valencia, Spain, 2014.
 Williamson. K., Questionnaires, individual interviews and focus group interviews. Research Methods (Second Edition) – Information, Systems, and Contexts, Tilde Publish-ing, Prahran, Australia, 2013.
 USDOT, FHWA/FTA, Public Involvement Techniques for Transportation Decision-Making. Chapter 3, Section B, Designing programs to bring out community viewpoints and resolve differences, U.S.DOT, Washington DC, 2002.
 Ahrens, G.-A., Sonderauswertung ‘Mobilität in Städten SrV 2013’, Technische Universität Dresden: Dresden, 2015.
 Francke, A., Lißner, S. & Becker, T., How can bicycle planning benefit from smartphone based data? A validation of Strava data. International Cycling Conference, 19–21 September 2017, Mannheim, Germany, 2017.
 Strava Inc., Global Heatmap. Strava, available at https://www.strava.com/heatmap#7.00/-120.90000/38.36000/blue/ride (accessed 23 March 2018).
 Bierlaire, M., Chen, J. & Newman, J., A probabilistic map matching method for smartphone GPS data. Transportation Research Part C, 26, pp. 78–98, 2013. DOI: 10.1016/j. trc.2012.08.001.
 Mansfeld, W., Satellitenortung und Navigation – Grundlagen, Wirkungsweise und Anwendung globaler Satellitennavigationssysteme, Vieweg&Teubner | Springer, Wiesbaden, 2010.
 Ortúzar, J.de D. & Willumsen, L.G., Modelling Transport, 4th ed., Wiley, New Jersey, 2011.
 McLafferty, I., Focus group interviews as a data collecting strategy, METHODOLOGICAL ISSUES IN NURSING RESEARCH. Journal of Advanced Nursing, 48(2), pp. 187–194, 2004. DOI: 10.1080/13645579.1998.10846874.