Skip to main content
Log in

On Bikes in Smart Cities

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Similar content being viewed by others

REFERENCES

  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. 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. 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. 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. How Bike Sharing Can Improve Urban Economic, Social & Environmental Performance. http://www.finchandbeak.com/1108/smart-cities-smart-transit-bike-shares.htm.

  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. Hoffmann, M.L., Bike Lanes are White Lanes: Bicycle Advocacy and Urban Planning, University of Nebraska Press, 2016.

    Book  Google Scholar 

  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. Moscow Bike-Sharing System. http://www.smoove-bike.com/news/moscow.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  13. Pedal-Powered Data. http://datasmart.ash.harvard.edu/news/article/pedal-powered-data-749.

  14. Smart City Bicycle and Pedestrian Counting. http://thinkingcities.com/smart-city-bicycle-and-pedestrian-counting-technology-released/.

  15. Vimoc. http://vimoc.com/product-2/.

  16. Axis Smart City. http://www.axis.com/files/brochure/bc_casestudies_safecities_en_1506_lo.pdf.

  17. Cognimatics Software. http://face.cognimatics.com/downloads/axis/bicycle/manualTVBAxisACAP.pdf.

  18. IVA 6.10 Intelligent Video Analysis. http://resource.boschsecurity.com/documents/DS_IVA_6.10_Data_ sheet_enUS_19245749387.pdf.

  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.

  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.

  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.

  22. Guruprasad, S., Morellas, V., and Papanikolopoulos, N., Deployment of Practical Methods for Counting Bicycle and Pedestrian Use of a Transportation Facility, 2012.

  23. Uke, N. and Thool, R., Moving vehicle detection for measuring traffic count using opencv, J. Autom. Control Eng., 2013, vol. 1, no. 4.

  24. Bike Counter. http://metrocount.com/shop/traffic-counters/40-mc5720-advanced-bicycle-counter.html.

  25. HI-TRAC CMU—Bicycle and Pedestrian Monitoring. http://www.jamartech.com/cmu.html.

  26. TRAX Cycles Plus. http://www.jamartech.com/bicyclecounting.html.

  27. Bicycle and pedestrian traffic counting devices. https://en.wikipedia.org/wiki/Traffic_count#Bicycle_and_ pedestrian_traffic_counting_devices.

  28. Bicycle and pedestrian traffic counting devices. http://www.lrrb.org/media/reports/201006.pdf.

  29. This $50 device could change bike planning forever. http://bikeportland.org/2015/01/13/50-device-change-bike-planning-forever-130891.

  30. Keep Your Bike Safer. http://www.sherlock.bike/.

  31. LoRaWAN bicycle tracking. http://www.mikroe.com/news/view/1180/a-bicycle-tracking-system-in-budapest-on-a-lorawan-network/.

  32. Internet of Bikes. http://www.nickhunn.com/nb-iot-the-internet-of-bikes-and-labradors/.

  33. GSMA Mobile IoT Initiative Welcomes First Low Power Wide Area Solutions at Mobile World Congress. http://www.businesswire.com/news/home/20160218005118/en/GSMA-Mobile-IoT-Initiative-Welcomes-Power-Wide.

  34. Huawei NB-IOT. http://www.huawei.com/minisite/4-5g/img/NB-IOT.pdf.

  35. SEMS. http://www.smart-ebikes.co.uk/.

  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.

  37. Zaragoza, H., Information retrieval: Algorithms and heuristics, Inf. Retr., 2002, vol. 5, nos. 2–3, pp. 271–274.

    Article  Google Scholar 

  38. Akbari, M., et al., From Tweets to wellness: Wellness event detection from Twitter streams, Thirtieth AAAI Conference on Artificial Intelligence, 2016.

  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. Zhu Zack, et al., Human activity recognition using social media data, Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia. ACM, 2013.

  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.

    Article  Google Scholar 

  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.

  43. Bicycle-sharing system. https://en.wikipedia.org/wiki/Bicycle-sharing_system.

  44. INSEAD: Bike-Share Systems: Accessibility and Availability. https://sites.insead.edu/facultyresearch/ research/doc.cfm?did=55916.

  45. Bike-sharing Chicago. http://www.divvybikes.com/.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  48. Schuijbroek, J., Hampshire, R., and van Hoeve, W.-J., Inventory Rebalancing and Vehicle Routing in Bike Sharing Systems, 2013.

  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.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  52. Forecasting Bike Sharing Demand. http://efavdb.com/bike-share-forecasting/.

  53. Data-Driven Policy: San Francisco just showed us how it should work. https://medium.com/@abhinemani/ data-driven-policy-san-francisco-just-showed-us-howit-should-work-c7725e0e2b40.

  54. Transbase. http://transbasesf.org/transbase/.

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Dmitry Namiot or Manfred Sneps-Sneppe.

Additional information

The article is published in the original.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dmitry Namiot, Manfred Sneps-Sneppe On Bikes in Smart Cities. Aut. Control Comp. Sci. 53, 63–71 (2019). https://doi.org/10.3103/S0146411619010085

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411619010085

Keywords:

Navigation