Crowdsourcing in bicycle traffic planning – MOVEBIS project presentation Rostock // Friday, September 27th 2019 Dr. Klemens Muthmann former TU Dresden, now Cyface GmbH. Data requirements by road infrastructure designers Who is driving? Where to drive? Who is driving? Where to drive? Where do routes start/end? How many drive? Where do routes start/end? How many drive? Why do we drive? Why do we drive? When toWhen drive? to drive? Folie 2 The development of road traffic data? Number of permanent counting points in large cities of central and east Germany 8 + short time counts 7 + household surveys 6 5 4 3 2 1 0 Chemnitz Dessau-Roßlau Dresden Eisenach Erfurt Gera Gotha Halle Jena Leipzig Magdeburg Weimar Zwickau Folie 3 How to close data gaps with GPS data? Share of Smartphone User in Germany in the years from 2012 to 2018 Cyclists as data source Strava BikeCitizens Komoot MOVEBIS Benefit for bicycle traffic design No established procedures! © Statista 2019, Source: Bitkom research Folie 4 Dresden 2018 MOVEBIS Räumliche Ausprägung des Radverkehrs Folie 5 Bild: P. Rosenkranz Limits of crowdsourcing and GPS …No Random Sample! Age Distribution of Strava-Users in Dresden 12% 85-94 Years 75-84 Years 88% 65-74 Years 55-64 Years 45-54 Years 35-44 Years 25-34 Years under 25 0 100 200 300 400 500 600 700 800 Women Men Folie 6 Types of cyclists Ambitious Passionate (Sports cyclist) (Enthusiastic cyclist) Pragmatic (Day-to-Day Functionale cyclist) (Sparse cyclist) Folie 7 Representativeness Daily Traffic Track Records 14% 12% 10% 8% 6% Share of Tracks Share 4% 2% 0% Heterogeneous CITY CYCLING 0:00:00 6:00:00 12:00:00 18:00:00 0:00:00 sample Time Beginning of Track SrV 2013 Beginning of Track Stadtradeln 2018 End of Track SrV 2013 End of Track Stadtradeln 2018 Folie 8 Representativeness Trip Duration 1.200 1.000 800 600 400 abs. Frequency abs. 200 - 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 >120 Trip Duration SrV 2013 Trip Duration Stadtradeln 2018 Trip Duration in [min] Folie 9 Representativeness Age and Gender Distribution in the City of Gießen SR 2018 40% 30% 50% 50% 20% Share male female 10% 0% ≤9 10-18 19-24 25-34 35-44 45-54 55-64 65-74 ≥75 Stadtradeln SrV2013 Age Category [Years] Folie 10 Results Trip Length Distribution Following Data Preparation 20% 18% - Representative trip length distribution 16% 14% - Important: Age and 12% Genderdistribution seems to be close to population 10% 8% - Sample can be filtered accordingly 6% 4% 2% 0% 4 7 9 14 19 24 29 34 35 44 49 54 59 64 69 74 more Datenreihen1 Datenreihen2 Strava-Data Folie 11 Data Usage Activities are Visible in Dataset Data Preparation is necessary! Folie 12 Data Usage Activities cleaned, Waiting Times remain! Folie 13 Bild: P. Rosenkranz Dresden 2018 Dresden 2018 Folie 14 Toolbox Bildquellen: Stephan Dinter, 2019 Dinter, Stephan Bildquellen: Folie 15 Bild: P. Rosenkranz www.movebis.org Presenter: [email protected] Contacts: [email protected] [email protected] [email protected] [email protected] [email protected] .