Abstract
Bicycles equipped with sensors, processing capacity and communications can be a promising source of data about the personal and the collective reality of urban cycling. While this concept has been attracting considerable interest, the key assumption is the design of a closed system where a uniform set of sensing bicycles, with a concrete set of sensors, is used to support a specific service. The core challenge, however, is how to generalise sensing approaches so that they can be collectively supported by many heterogeneous bicycles, owned by a multitude of entities, and integrated into a common ecosystem of urban data. In this work, we provide a comprehensive analysis of the design space for on-bike sensing. We consider a diverse set of sensing alternatives, the potential value propositions associated with their data, and the collective perspective of how to optimise sensing by exploring the complementarities between heterogeneous bicycles. This broader perspective should inform the design of more effective sensing strategies that can maximise the overall value generated by bicycles in smart cycling ecosystems and enable new cycling services.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Stratta, P.: Towards a Smarter Cycling. On the brink of a Smart (R)evolution. https://ecf.com/what-we-do/cycling-new-technologies/towards-smarter-cycling
Nikolaeva, A., te Brömmelstroet, M., Raven, R., Ranson, J.: Smart cycling futures: charting a new terrain and moving towards a research agenda. J. Transp. Geogr. 79, 102486 (2019)
Lee, J., Leem, Y.T., Lee, S.H.: Categorizing u-bike service and assessing its adoptability under it. In: 12th World Conference on Transportation and Research, pp. 1–10 (2010)
Elen, B., et al.: The Aeroflex: a bicycle for mobile air quality measurements. Sensors (Switzerland) 13, 221–240 (2013)
Outram, C., Ratti, C., Bitterman, A.: The Copenhagen Wheel: An innovative electric bicycle system that harnesses the power of real-time information and crowd sourcing. In: Mayor’s Summit at the COP15 United Nations Climate Change Conference (2009)
SODAQ: SODAQ- Sniffer Bike. https://sodaq.com/projects/sniffer-bike/
Wirtschaft, W., S. des Van der H.K.– T. und: Feintuning für die Radverkehrsplanung, https://www.hs-karlsruhe.de/presse/feintuning-fuer-die-radverkehrsplanung/
Kiefer, C., Behrendt, F.: Smart e-bike monitoring system: real-time open source and open hardware GPS assistance and sensor data for electrically-assisted bicycles. IET Intell. Transp. Syst. 10, 79–88 (2016)
Eisenman, S.B., Miluzzo, E., Lane, N.D., Peterson, R.A., Ahn, G.S., Campbell, A.T.: BikeNet: a mobile sensing system for cyclist experience mapping. ACM Trans. Sens. Networks. 6 (2009)
See.Sense: See.Sense. https://seesense.cc/
Makarova, I.V., Boyko, A.D., Shubenkova, K.A.: Cycling intellectualization in Smart Cities. EAI Endors. Trans. Smart Cities 2, 153498 (2017)
Shen, S., Wei, Z.Q., Sun, L.J., Su, Y.Q., Wang, R.C., Jiang, H.M.: The shared bicycle and its network—internet of shared bicycle (IoSB): a review and survey. Sensors (Switzerland) 18, 1–24 (2018)
Ehrgott, M., Wang, J.Y.T., Raith, A., Van Houtte, C.: A bi-objective cyclist route choice model. Transp. Res. Part A Policy Pract. 46, 652–663 (2012)
Winters, M., Davidson, G., Kao, D., Teschke, K.: Motivators and deterrents of bicycling: Comparing influences on decisions to ride. Transportation (Amst) (2011)
Segadilha, A.B.P., Sanches, S.D.P.: Identification of Factors that Influence, Cyclistś, Route Choice. Procedia Soc. Behav. Sci. 160, 372–380 (2014)
Félix, R.: Barriers and motivators to bicycle in low cycling maturity cities: Lisbon case study, 259 (2019)
Broach, J., Dill, J., Gliebe, J.: Where do cyclists ride? A route choice model developed with revealed preference GPS data. Transp. Res. Part A Policy Pract. 46, 1730–1740 (2012)
Su, J.G., Winters, M., Nunes, M., Brauer, M.: Designing a route planner to facilitate and promote cycling in Metro Vancouver, Canada. Transp. Res. Part A Policy Pract. 44, 495–505 (2010)
Silva, N., Soares, J., Shah, V., Santos, M.Y., Rodrigues, H.: Anomaly detection in roads with a data mining approach. Procedia Comput. Sci. 121, 415–422 (2017)
Wickham, H.: Tidy data. J. Stat. Softw. 59 (2014).
Liang, S., Huang, C.Y., Khalafbeigi, T.: OGC SensorThings API Part 1: sensing. open geospatial consortium. Implement. Stand. 1–105 (2016)
Liu, X., Xiang, C., Li, B., Jiang, A.: Collaborative bicycle sensing for air pollution on roadway. In: Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligent Computing, 2015 IEEE 12th International Conference on Advanced Trusted Computing, 2015 IEEE 15th International Conference on Scalable Computing and Communication, vol. 20, pp. 316–319 (2016)
Corno, F., Montanaro, T., Migliore, C., Castrogiovanni, P.: SmartBike: an IoT crowd sensing platform for monitoring city air pollution. Int. J. Electr. Comput. Eng. 7, 3602–3612 (2017)
Boot, M.: A digital future for cycling: the role of data and ITS in encouraging more cyclists. https://www.intelligenttransport.com/transport-articles/78216/digital-future-cycling-data-its-encouraging-cyclists/
Acknowledgements
This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039334; Funding Reference: POCI-01–0247-FEDER-039334].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Cabral, R., Peixoto, E., Carvalho, C., José, R. (2021). An Ecosystem Approach to the Design of Sensing Systems for Bicycles. In: Paiva, S., Lopes, S.I., Zitouni, R., Gupta, N., Lopes, S.F., Yonezawa, T. (eds) Science and Technologies for Smart Cities. SmartCity360° 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-76063-2_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-76063-2_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76062-5
Online ISBN: 978-3-030-76063-2
eBook Packages: Computer ScienceComputer Science (R0)