![](bg8.png)
number of users to be automatically created, another
option may be to simply make user registration non-
scriptable e.g. by using captchas or requiring a short
cycling sequence with pulse rate (for example, by us-
ing the front smartphone camera and pulse-by-face).
ACKNOWLEDGEMENTS
This project is currently funded by the Austrian Re-
search Promotion Agency (FFG) and by the Austrian
Federal Ministry for Climate action, Environment,
Energy, Mobility, Innovation and Technology (BMK)
as project Cycle4Value (873384).
REFERENCES
Buhl, H. U., Schweizer, A., and Urbach, N. (2017).
Blockchain-Technologie als Schl
¨
ussel f
¨
ur die
Zukunft. Zeitschrift f
¨
ur das gesamte Kreditwesen,
pages 596–599.
Charv
´
atov
´
a, H., Proch
´
azka, A., Vaseghi, S., Vy
ˇ
sata, O., and
Vali
ˇ
s, M. (2017). GPS-based analysis of physical ac-
tivities using positioning and heart rate cycling data.
Signal, Image and Video Processing, 11(2):251–258.
Cohen, W. (1995). Fast effective rule induction. In Pro-
ceedings of the Twelfth International Conference on
Machine Learning, San Francisco, CA, 1995, pages
115–123. Morgan Kaufmann.
Dabiri, S., Lu, C.-T., Heaslip, K., and Reddy, C. K. (2019).
Semi-supervised deep learning approach for trans-
portation mode identification using GPS trajectory
data. IEEE Transactions on Knowledge and Data En-
gineering, 32(5):1010–1023.
Etemad, M. (2018). Transportation Modes Classifica-
tion Using Feature Engineering. arXiv preprint
arXiv:1807.10876.
Feng, T. and Timmermans, H. J. (2013). Transportation
mode recognition using GPS and accelerometer data.
Transportation Research Part C: Emerging Technolo-
gies, 37:118–130.
Hopf, S. and Picot, A. (2018). Revolutioniert Blockchain-
Technologie das Management von Eigentumsrechten
und Transaktionskosten? In Interdisziplin
¨
are Perspek-
tiven zur Zukunft der Wertsch
¨
opfung, pages 109–119.
Springer.
Illek, G. and Mayer, I. (2013). Radverkehr in Zahlen–
Daten, Fakten und Stimmungen. BMVIT, Wien.
Nawaz, A., Zhiqiu, H., Senzhang, W., Hussain, Y., Khan,
I., and Khan, Z. (2020). Convolutional LSTM based
transportation mode learning from raw GPS trajecto-
ries. IET Intelligent Transport Systems, 14(6):570–
577.
Nitsche, P., Widhalm, P., Breuss, S., Br
¨
andle, N., and
Maurer, P. (2014). Supporting large-scale travel sur-
veys with smartphones–A practical approach. Trans-
portation Research Part C: Emerging Technologies,
43:212–221.
No
¨
el Racine, A., Garbarino, J.-M., Corrion, K., D’Arripe-
Longueville, F., Massiera, B., and Vuillemin, A.
(2020). Perceptions of barriers and levers of health-
enhancing physical activity policies in mid-size french
municipalities. Health research policy and systems,
18:1–10.
Soares, E. F. d. S., Salehinejad, H., Campos, C. A. V.,
and Valaee, S. (2019). Recurrent Neural Networks
for Online Travel Mode Detection. In 2019 IEEE
Global Communications Conference (GLOBECOM),
pages 1–6. IEEE.
Thomas, A., Koenig, N., Higgins, T., Black, M., Pfeiffer,
A., Donelan, L., Lenzen, B., Muniz, N., Patel, K.,
Taylan, A., and Wernbacher, T. (2019). From Learn-
ing to Assessment, How to Utilize Blockchain Tech-
nologies in Gaming Environments to Secure Learning
Outcomes and Test Results, page 172. Malta College
of Arts, Science and Technology.
Tomschy, R. and Steinacher, I. (2017).
¨
Osterreich unter-
wegs. . . mit dem Fahrrad. BUNDESMINISTERIUM
F
¨
UR VERKEHR, IUT, WIEN (ed.).
Van Hoye, A., Vandoorne, C., Absil, G., Lecomte, F., Fal-
lon, C., Lombrail, P., and Vuillemin, A. (2019). Health
enhancing physical activity in all policies? compari-
son of national public actors between france and bel-
gium. Health Policy, 123(3):327–332.
Vu, T. H., Dung, L., and Wang, J.-C. (2016). Transporta-
tion mode detection on mobile devices using recurrent
nets. In Proceedings of the 24th ACM international
conference on Multimedia, pages 392–396.
Weatherson, K. A., McKay, R., Gainforth, H. L., and Jung,
M. E. (2017). Barriers and facilitators to the imple-
mentation of a school-based physical activity policy in
canada: application of the theoretical domains frame-
work. BMC public health, 17(1):835.
Zheng, Y., Xie, X., Ma, W.-Y., et al. (2010). GeoLife: A
collaborative social networking service among user,
location and trajectory. IEEE Data Eng. Bull.,
33(2):32–39.
Zheng, Y., Zhang, L., Xie, X., and Ma, W.-Y. (2009). Min-
ing interesting locations and travel sequences from
GPS trajectories. In Proceedings of the 18th interna-
tional conference on World wide web, pages 791–800.
Cycle4Value: A Blockchain-based Reward System to Promote Cycling and Reduce CO2 Footprint
1089