Abstract
The new generation of bike-sharing services without docking stations is spreading around large cities of the world. The paper provides a technical specification of a platform, for managing a dockless bike sharing system. The bicycles of the platform are equipped with GPS devices and GPRS cards that can transmit, over the Internet, their exact location at any time. We collect and store all events derived from a user’s interaction with the system and in addition the trajectory points of a route during a rent order. The platform aims to fulfill the requirements of bikers, administrators and the research community through the collection, analysis and exploitation of bike sharing data.
In the context of the platform, an app for smart devices is implemented for citizens to access the system. A dashboard is offered to the administrator as a valuable tool to inspect, promote the system and evaluate its usage. Last, all stored anonymised data can be accessible for further analysis by the research community through a REST API. The i-CHANGE platform is currently pilot tested in the city of Thessaloniki, Greece.
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App checks for valid email formats name@domain and if no other registered user exists with the same address.
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Choice among 2, 5, 10, 20 and 50 Euro.
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Mudguard, Large Basket, Exposure Area, Bicycle Frame, Electrical Assistance, Brakes, Lights, Seat Height Adjustment Lever, Saddle, Kickstand, Front Wheel, Rear Wheel, Lock.
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It refers to the identifier after the process of anonymisation.
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References
Ai, Y., et al.: A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Comput. Appl. 31(5), 1665–1677 (2018). https://doi.org/10.1007/s00521-018-3470-9
Fishman, E.: Bikeshare: a review of recent literature. Transp. Rev. 36(1), 92–113 (2016)
Global, M.: Beijing Tsinghua Tongheng Planning and Design Institute, & China New Urbanization Research Institute, 19 May 2017. The mobike white paper: Bike-share in the city
Han, J., Kamber, M., Tung, A.K.: Spatial clustering methods in data mining. In: Geographic Data Mining and Knowledge Discovery, pp. 188–217 (2001)
Li, Y., Shuai, B.: Origin and destination forecasting on dockless shared bicycle in a hybrid deep-learning algorithms. Multimed. Tools Appl. 79, 5269–5280 (2018)
McKenzie, G.: Urban mobility in the sharing economy: a spatiotemporal comparison of shared mobility services. Comput. Environ. Urban Syst. 79, 101418 (2020)
Roy, P.R., Bilodeau, G.-A.: Road user abnormal trajectory detection using a deep autoencoder. In: Bebis, G., et al. (eds.) ISVC 2018. LNCS, vol. 11241, pp. 748–757. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03801-4_65
Schinas, M., Papadopoulos, S., Apostolidis, L., Kompatsiaris, Y., Mitkas, P.A.: Open-source monitoring, search and analytics over social media. In: Kompatsiaris, I., et al. (eds.) INSCI 2017. LNCS, vol. 10673, pp. 361–369. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70284-1_28
Shen, Y., Zhang, X., Zhao, J.: Understanding the usage of dockless bike sharing in Singapore. Int. J. Sustain. Transp. 12(9), 686–700 (2018)
Torrisi, V., Ignaccolo, M., Inturri, G.: Innovative transport systems to promote sustainable mobility: developing the model architecture of a traffic control and supervisor system. In: Gervasi, O., et al. (eds.) ICCSA 2018. LNCS, vol. 10962, pp. 622–638. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95168-3_42
Xu, C., Ji, J., Liu, P.: The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets. Transp. Res. Part C: Emerg. Technol. 95, 47–60 (2018)
Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)
Acknowledgements
This research was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code:T1EDK-04582)
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Apostolidis, L. et al. (2020). i-CHANGE: A Platform for Managing Dockless Bike Sharing Systems. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12250. Springer, Cham. https://doi.org/10.1007/978-3-030-58802-1_61
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DOI: https://doi.org/10.1007/978-3-030-58802-1_61
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