Abstract
In the past years, mobility trends in cities around the world have been pushing for safer, greener, and more efficient transportation systems. This shift in mobility trends creates an opportunity for using mobile lightweight infrastructure, such as bicycles, as a generator of knowledge that will benefit commuters alongside the environmental and societal performance of cities. We propose a system architecture design for an open source mobile sensor fusion apace a platform with a knowledge abstraction framework that enables citizens, urban planners, researchers, and city officials to better address the complex issues that are innate to cities. The system is mounted on a commercial electric assist bike and is able to combine sensor input that describes the bicycle’s electro-mechanical, geospatial, and environmental states. The system proposes sensor flexibility and modularity as key characteristics, and the abstraction framework conceptualizes the way in which these characteristics can be best exploited for city improvement. We demonstrate the functionality of the system and framework through the creation of a use case implementation for clustering bike trip patterns using unsupervised learning clustering techniques. This platform outlines a way to migrate focus from providing solutions to asking the right questions in order to satisfy citizens’ needs.
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
Notes
- 1.
The bikes used in this study provides assist torque in addition to human pedalling. This bicycle does not drive solely from the motor unit.
- 2.
Camera module is not used on the use case implementation seen in Sect. 4 but has been installed for future work on the platform.
- 3.
While the two way communication with the motor unit is only being used by the activation button, as the platform is implemented on different uses, the communication can become relevant to enhance the user’s interaction with the bicycle
References
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., Rahwan, I.: The moral machine experiment. Nature 563(7729), 59–64 (2018)
Berke, A.: Income, race, bikes (2019). https://www.media.mit.edu/projects/income-race-bikes/overview/. Accessed 29 April 2020
Coretti, N., Pastor, L.A., Larson, K.: Autonomous bicycles: a new approach to bicycle-sharing systems. In: International IEEE Conference on Intelligent Transportation Systems IEEE ITSC (2020, Accepted)
Elbasiony, R.M., Sallam, E.A., Eltobely, T.E., Fahmy, M.M.: A hybrid network intrusion detection framework based on random forests and weighted k-means. Ain Shams Eng. J. 4(4), 753–762 (2013)
Elmenreich, W.: A review on system architectures for sensor fusion applications. In: IFIP International Workshop on Software Technolgies for Embedded and Ubiquitous Systems, pp. 547–559. Springer (2007)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37–37 (1996)
Fishman, E., Washington, S., Haworth, N.: Bike share: a synthesis of the literature. Transp. Rev. 33(2), 148–165 (2013)
Python Software Foundation. Uuid. https://docs.python.org/3/library/uuid.html#module-uuid, 2002–2020. Accessed 10 Mar 2020
Gaddam, S.R., Phoha, V.V., Balagani, K.S.: K-means+ id3: a novel method for supervised anomaly detection by cascading k-means clustering and id3 decision tree learning methods. IEEE Trans. Knowl. Data Eng. 19(3), 345–354 (2007)
Green, B.: The Smart Enough City: Putting Technology in its Place to Reclaim our Urban Future. MIT Press, Cambridge (2019)
Gupta, P., Kumar P.: Behavior study of bike driver and alert system using IoT and cloud. In: Proceedings of ICRIC 2019, Lecture Notes in Electrical Engineering, pp. 579–593 (2019)
Head, B.W., et al.: Wicked problems in public policy. Public policy, 3(2), 101 (2008)
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(2), 79–88 (2014)
Scikit learn Developers. Clustering, 2007-2019. https://scikit-learn.org/stable/modules/clustering.html. Accessed 27 April 2020
Lessig, L.: The new chicago school. J. Legal Stud. 27(S2), 661–691 (1998)
Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recogn. 36(2), 451–461 (2003)
Lin, M.: Persuasive electric vehicle (pev) 2015-2020. https://www.media.mit.edu/projects/pev/overview/. Accessed 29 April 2020
Markin, M., Harris, C., Bernhardt, M., Austin, J., Bedworth, M., Greenway, P., Johnston, R., Little, A., Lowe, D.: Technology foresight on data fusion and data processing. Publication of The Royal Aeronautical Society (1997)
Millonig, A., Wunsch, M., Stibe, A., Seer, S., Dai, C., Schechtner, K., Chin, R.C.C.: Gamification and social dynamics behind corporate cycling campaigns. Transp. Res. Procedia 19, 33–39 (2016)
Muniyandi, A.P., Rajeswari, R., Rajaram, R.: Network anomaly detection by cascading k-means clustering and c4.5 decision tree algorithm. Procedia Engineering 30, 174–182 (2012)
Münz, G., Li, S., Carle, G.: Traffic anomaly detection using k-means clustering. In GI/ITG Workshop MMBnet, pp. 13–14 (2007)
Namiot, D., Sneps-Sneppe, M.: On bikes in smart cities. Automat. Control Comput. Sci. 53(1), 67–71 (2019)
Nawyn, J., Smuts, C., Larson, K.: A visualization tool for reconstructing behavior patterns in built spaces. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, pp. 269–272 (2017)
Patel, V.R., Mehta, R.G.: Impact of outlier removal and normalization approach in modified k-means clustering algorithm. Int. J. Comput. Sci. Issues (IJCSI) 8(5), 331 (2011)
Sadik-Khan, J., Solomonow, S.: Streetfight: Handbook for an urban revolution. Penguin, New York (2017)
Smuts, C.: Termites (2018). http://termites.synthetic.space/. Accessed 20 April 2020
Swamy, U.B.M., Khuddus, A.: A Smart bike. In: 2019 1st International Conference on Advances in Information Technology (ICAIT), pp. 462–468 (2019)
Swathi, S.J., Raj, S., Devaraj, D.: Microcontroller and sensor based smart biking system for driver’s safety. In: 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), pp. 1–5 (2019)
Yao, J.W.-H.: IDK: An Interaction Development Kit to design interactions for lightweight autonomous vehicles. Master’s thesis, Massachusetts Institute of Technology (2019)
Zhang, Y., Chen, K., Yi, J.: Rider trunk and bicycle pose estimation with fusion of force/inertial sensors. IEEE Trans. Biomed. Eng. 60(9), 2541–2551 (2013)
Acknowledgments
The authors of this publication thank Life Solutions Company, Panasonic Corporation (Jin Yoshizawa, Nanako Yamasaki, Yoshio Shimbo) as well as Panasonic Cycle Technology Co., Ltd. (Hiroyuki Kamo) for the financial and technological support given for the development of this project. Specifically, the development and provision of the hackable micro unit (internal bicycle sensor) which acts as a pillar to the JettSen system along with the Jetter e-bicycle which was used for prototyping and testing the system.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Rico, A., Sakai, Y., Larson, K. (2021). JettSen: A Mobile Sensor Fusion Platform for City Knowledge Abstraction. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 2 . FTC 2020. Advances in Intelligent Systems and Computing, vol 1289. Springer, Cham. https://doi.org/10.1007/978-3-030-63089-8_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-63089-8_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63088-1
Online ISBN: 978-3-030-63089-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)