Automatic Control and Computer Sciences

, Volume 53, Issue 1, pp 63–71 | Cite as

On Bikes in Smart Cities

  • Dmitry NamiotEmail author
  • Manfred Sneps-SneppeEmail author


In this paper, we discuss data models and data mining for bicycles in Smart Cities. Mobility issues (or Smart Mobility) are one of the main components of Smart Cities. Bicycles, as a transport component in the cities, are on the rise all over the world. At least, it is true for all areas where the climate even minimally allows it. The reasons are quite obvious. This is democratic and accessible this type of transport, it is cheap and environmental friendliness. Of course, the promotion of a healthy lifestyle also plays its role. The development of this type of transport (like any other) has many different aspects. In this paper, we dwell on the issues of tracking the movement of cyclists and planning bike-sharing systems. All this information will serve as a set of metrics for any design in Smart Cities.


Smart City smart bike mobility bike-sharing 


  1. 1.
    Namiot, D., et al., Pedestrians in the Smart City, Int. J. Open Inf. Technol., 2016, vol. 4, no. 10, pp. 15–21.Google Scholar
  2. 2.
    Namiot, D., et al., Pedestrians in the Smart City, Int. J. Open Inf. Technol., 2016, vol. 4, no. 10, pp. 9–14.Google Scholar
  3. 3.
    Benevolo, C., Dameri, R.P., and D’Auria, B., Smart mobility in smart city, in Empowering Organizations, Springer International Publishing, 2016, pp. 13–28.Google Scholar
  4. 4.
    Dameri, R.P., Smart City and ICT. Shaping urban space for better quality of life, in Information and Communication Technologies in Organizations and Society, Springer International Publishing, 2016.Google Scholar
  5. 5.
    How Bike Sharing Can Improve Urban Economic, Social & Environmental Performance. Scholar
  6. 6.
    Midgley, P., The role of smart bike-sharing systems in urban mobility, Journeys, 2009, vol. 2, no. 1, pp. 23–31.Google Scholar
  7. 7.
    Hoffmann, M.L., Bike Lanes are White Lanes: Bicycle Advocacy and Urban Planning, University of Nebraska Press, 2016.CrossRefGoogle Scholar
  8. 8.
    Pucher, J., Buehler, R., and Seinen, M., Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies, Transp. Res. Part A: Policy Pract., 2011, vol. 45, no. 6, pp. 451–475.Google Scholar
  9. 9.
    Moscow Bike-Sharing System. Scholar
  10. 10.
    Opiela, K.S., Snehamay Khasnabis, and Datta, T.K., Determination of the characteristics of bicycle traffic at urban intersections, Transp. Res. Rec., 1980, vol. 743, pp. 30–38.Google Scholar
  11. 11.
    Zhang Jun, et al., Comparative analysis of pedestrian, bicycle and car traffic moving in circuits, Procedia-Soc. Behav. Sci., 2013, vol. 104, pp. 1130–1138.CrossRefGoogle Scholar
  12. 12.
    El Esawey, M., et al., Development of daily adjustment factors for bicycle traffic, J. Transp. Eng., 2013, vol. 139, no. 8, pp. 859–871.CrossRefGoogle Scholar
  13. 13.
    Pedal-Powered Data. Scholar
  14. 14.
    Smart City Bicycle and Pedestrian Counting. Scholar
  15. 15.
    Vimoc. Scholar
  16. 16.
    Axis Smart City. Scholar
  17. 17.
    Cognimatics Software. Scholar
  18. 18.
    IVA 6.10 Intelligent Video Analysis. sheet_enUS_19245749387.pdf.Google Scholar
  19. 19.
    Heikkilä Janne and Olli Silvén, A real-time system for monitoring of cyclists and pedestrians, Image Vision Comput., 2004, vol. 22, no. 7, pp. 563–570.Google Scholar
  20. 20.
    Ponte, G., et al., Using specialised cyclist detection software to count cyclists and determine cyclist travel speed from video, Australasian Road Safety Research Policing Education Conference, 2014, Melbourne, Victoria, Australia, 2014.Google Scholar
  21. 21.
    Guruprasad, S., Morellas, V., and Papanikolopoulos, N., Counting pedestrians and bicycles in traffic scenes, 2009 12th International IEEE Conference on Intelligent Transportation Systems, 2009.Google Scholar
  22. 22.
    Guruprasad, S., Morellas, V., and Papanikolopoulos, N., Deployment of Practical Methods for Counting Bicycle and Pedestrian Use of a Transportation Facility, 2012.Google Scholar
  23. 23.
    Uke, N. and Thool, R., Moving vehicle detection for measuring traffic count using opencv, J. Autom. Control Eng., 2013, vol. 1, no. 4.Google Scholar
  24. 24.
    Bike Counter. Scholar
  25. 25.
    HI-TRAC CMU—Bicycle and Pedestrian Monitoring. Scholar
  26. 26.
    TRAX Cycles Plus. Scholar
  27. 27.
    Bicycle and pedestrian traffic counting devices. pedestrian_traffic_counting_devices.Google Scholar
  28. 28.
    Bicycle and pedestrian traffic counting devices. Scholar
  29. 29.
    This $50 device could change bike planning forever. Scholar
  30. 30.
    Keep Your Bike Safer. Scholar
  31. 31.
    LoRaWAN bicycle tracking. Scholar
  32. 32.
    Internet of Bikes. Scholar
  33. 33.
    GSMA Mobile IoT Initiative Welcomes First Low Power Wide Area Solutions at Mobile World Congress. Scholar
  34. 34.
    Huawei NB-IOT. Scholar
  35. 35.
    SEMS. Scholar
  36. 36.
    Kiefer, C. and Behrendt, F., Smart E-Bike Monitoring System: Realtime open-source and open hardware GPS, assistance and sensor data for electrically-assisted bicycles, J. IET Intell. Transp. Syst., 2015, pp. 1–10.Google Scholar
  37. 37.
    Zaragoza, H., Information retrieval: Algorithms and heuristics, Inf. Retr., 2002, vol. 5, nos. 2–3, pp. 271–274.CrossRefGoogle Scholar
  38. 38.
    Akbari, M., et al., From Tweets to wellness: Wellness event detection from Twitter streams, Thirtieth AAAI Conference on Artificial Intelligence, 2016.Google Scholar
  39. 39.
    Zhou Deyu, Liangyu Chen, and Yulan He, An unsupervised framework of exploring events on Twitter: Filtering, extraction and categorization, AAAI, 2015.Google Scholar
  40. 40.
    Zhu Zack, et al., Human activity recognition using social media data, Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia. ACM, 2013.Google Scholar
  41. 41.
    Kaltenbrunner, A., et al., Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system, Pervasive Mobile Comput., 2010, vol. 6, no. 4, pp. 455–466.CrossRefGoogle Scholar
  42. 42.
    Kuo Yin-Hsi, et al., Discovering the city by mining diverse and multimodal data streams, Proceedings of the 22nd ACM International Conference on Multimedia. ACM, 2014.Google Scholar
  43. 43.
    Bicycle-sharing system. Scholar
  44. 44.
    INSEAD: Bike-Share Systems: Accessibility and Availability. research/doc.cfm?did=55916.Google Scholar
  45. 45.
    Bike-sharing Chicago. Scholar
  46. 46.
    Vogel, P., Greiser, T., and Mattfeld, D.C., Understanding bike-sharing systems using data mining: Exploring activity patterns, Procedia-Soc. Behav. Sci., 2011, vol. 20, pp. 514–523.CrossRefGoogle Scholar
  47. 47.
    O’Brien, O., Cheshire, J., and Batty, M., Mining bicycle sharing data for generating insights into sustainable transport systems, J. Transp. Geogr., 2014, vol. 34, pp. 262–273.CrossRefGoogle Scholar
  48. 48.
    Schuijbroek, J., Hampshire, R., and van Hoeve, W.-J., Inventory Rebalancing and Vehicle Routing in Bike Sharing Systems, 2013.Google Scholar
  49. 49.
    Chemla, D., Meunier, F., and Wolfler Cavolo, R., Bike sharing systems: Solving the static rebalancing problem, Discrete Optim., 2013, vol. 10, no. 2, pp. 120–146.MathSciNetCrossRefzbMATHGoogle Scholar
  50. 50.
    Pfrommer, J., et al., Dynamic vehicle redistribution and online price incentives in shared mobility systems, IEEE Trans. Intell. Transp. Syst., 2014, vol. 15, no. 4, pp. 1567–1578.CrossRefGoogle Scholar
  51. 51.
    Kloimüllner, C., et al., Balancing bicycle sharing systems: An approach for the dynamic case, European Conference on Evolutionary Computation in Combinatorial Optimization, Springer Berlin Heidelberg, 2014.Google Scholar
  52. 52.
    Forecasting Bike Sharing Demand. Scholar
  53. 53.
    Data-Driven Policy: San Francisco just showed us how it should work. data-driven-policy-san-francisco-just-showed-us-howit-should-work-c7725e0e2b40.Google Scholar
  54. 54.
    Transbase. Scholar

Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  1. 1.Lomonosov Moscow State UniversityMoscowRussia
  2. 2.Ventspils University CollegeVentspilsLatvia

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