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Dr. Klemens Muthmann former TU Dresden, now Cyface GmbH. Crowdsourcing in bicycle traffic planning – MOVEBIS project presentation Rostock // Friday, September 27th 2019
Dr. Klemens Muthmann
former TU Dresden, now Cyface GmbH.
Crowdsourcing in bicycle traffic planning –
MOVEBIS project presentation
Rostock // Friday, September 27th 2019
Folie 2
Data requirements by road infrastructure designers
Where to drive? Where to drive?
When to drive? When to drive?
How many drive? How many drive?
Who is driving? Who is driving?
Why do we drive? Why do we drive?
Where do routes start/end? Where do routes start/end?
Folie 3
The development of road traffic data?
0
1
2
3
4
5
6
7
8
Chemnitz Dessau-Roßlau Dresden Eisenach Erfurt Gera Gotha Halle Jena Leipzig Magdeburg Weimar Zwickau
Number of permanent counting points in large cities of central and east Germany
+ short time counts
+ household surveys
Folie 4
How to close data gaps with GPS data?
Cyclists as data source
Strava
BikeCitizens
Komoot
MOVEBIS
Benefit for bicycle traffic design
No established procedures!
© Statista 2019, Source: Bitkom research
Share of Smartphone User in Germany in the years from 2012 to 2018
Folie 5
MOVEBIS
Räumliche Ausprägung des Radverkehrs
Dresden 2018
Bild: P. Rosenkranz
Folie 6
0 100 200 300 400 500 600 700 800
under 25
25-34 Years
35-44 Years
45-54 Years
55-64 Years
65-74 Years
75-84 Years
85-94 Years
Age Distribution of Strava-Users in Dresden
Women Men
Limits of crowdsourcing and GPS
…No Random Sample!
88%
12%
Folie 7
Types of cyclists
Passionate
(Enthusiastic cyclist)
Ambitious (Sports cyclist)
Pragmatic
(Day-to-Day cyclist)
Functionale
(Sparse cyclist)
Folie 8
Representativeness
Heterogeneous CITY CYCLING sample
0%
2%
4%
6%
8%
10%
12%
14%
0:00:00 6:00:00 12:00:00 18:00:00 0:00:00
Shar
e o
f Tr
acks
Time
Daily Traffic Track Records
Beginning of Track SrV 2013 Beginning of Track Stadtradeln 2018
End of Track SrV 2013 End of Track Stadtradeln 2018
Folie 9
Representativeness
-
200
400
600
800
1.000
1.200
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
abs.
Fre
qu
en
cy
Trip Duration in [min]
Trip Duration
Trip Duration SrV 2013 Trip Duration Stadtradeln 2018
Folie 10
Representativeness
0%
10%
20%
30%
40%
≤9 10-18 19-24 25-34 35-44 45-54 55-64 65-74 ≥75
Shar
e
Age Category [Years]
Age and Gender Distribution in the City of Gießen SR 2018
Stadtradeln SrV2013
50%
50%
male female
Folie 11
Results
Following Data Preparation
- Representative trip length
distribution
- Important: Age and
Genderdistribution seems to be close to population
- Sample can be filtered accordingly
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
4 7 9 14 19 24 29 34 35 44 49 54 59 64 69 74 more
Trip Length Distribution
Datenreihen1 Datenreihen2 Strava-Data
Folie 12
Data Usage
Activities are Visible in Dataset Data Preparation is
necessary!
Folie 13
Data Usage
Activities cleaned, Waiting Times remain!
Folie 14
Dresden 2018 Dresden 2018
Bild: P. Rosenkranz
Folie 15
Toolbox
Bild
qu
elle
n: S
tep
ha
n D
inte
r, 2
01
9
Bild: P. Rosenkranz
