Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Curvature-based distribution algorithm: rebalancing bike sharing system with agent-based simulation

  • 244 Accesses

  • 3 Citations

Abstract

The selected rebalancing strategy determines the service results for bike sharing system (BSS). All cities have different operators, such as the number of stations, bikes, and rebalancing trucks, traffic congestion patterns, terrain maps, and maintenance costs. Therefore, each city needs a rebalancing strategy of its own. However, identifying an appropriate rebalancing strategy requires a method that can simulate a large volume of rebalancing operators. In this paper, we propose a novel method to dynamically rebalance BSS with agent-based simulation. We developed a curvature-based distribution algorithm which allows simulations with a large number of rebalancing trucks and stations (40 trucks and 581 stations), compared to previous studies on bike rebalancing. The algorithm uses a three-dimensional (3D) terrain map created with a large volume of BSS usage data (3,595,383 observations) to generate dynamic truck routes for agent-based simulation. In addition, traffic congestion data were used as weighting values to improve the accuracy of the proposed method for truck route generation. Thus, the proposed algorithm can test different rebalancing strategies that are suitable for a specific city with various spatiotemporal conditions. Specifically, the system analyst can simulate different truck numbers, working ranges, and working hours to identify a suitable strategy for various cities to estimate yearly budget. The algorithm is simple to implement and adaptive to various bike usage data. The research also proposes a visual analysis method based on simulated results of rebalance imbalance metrics, service failure rate, number of daily operations, daily truck travel distance, and stochastic bike usage behavior.

Graphical abstract

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

References

  1. Andrienko N, Andrienko G, Gatalsky P (2003) Exploratory spatio-temporal visualization: an analytical review. J Vis Lang Comput 14:503–541

  2. Batty M (2001) Exploring isovist fields: space and shape in architectural and urban morphology. Environ Plan B Plan Des 28:123–150

  3. Chemla D, Meunier F, Calvo RW (2013) Bike sharing systems: solving the static rebalancing problem. Discret Optim 10:120–146

  4. Chiariotti F, Pielli C, Zanella A, Zorzi M (2018) A dynamic approach to rebalancing bike-sharing systems. Sensors 18:512

  5. Chicago Traffic Tracker Data Set. https://data.cityofchicago.org/Transportation/Chicago-Traffic-Tracker-Historical-Congestion-Esti/emtn-qqdi. Accessed 3 July 2018

  6. Conticelli E, Santangelo A, Tondelli S (2018) Innovations in cycling mobility for sustainable cities. In: Town and infrastructure planning for safety and urban quality: proceedings of the XXIII international conference on living and walking in cities (LWC 2017), 2017, Brescia, Italy, pp 155–162

  7. de Chardon CM, Caruso G, Thomas I (2016) Bike-share rebalancing strategies, patterns, and purpose. J Transp Geogr 55:22–39

  8. Divvy Data Set. https://www.divvybikes.com/system-data. Accessed 24 April 2018

  9. Do M, Noh YS (2014) Analysis of the affecting factors on the bike-sharing demand focused on Daejeon City. J Korean Soc Civ Eng 34:1517–1524

  10. Dötterl J, Bruns R, Dunkel J, Ossowski S (2017) Towards dynamic rebalancing of bike sharing systems: an event-driven agents approach. In: Portuguese conference on artificial intelligence, pp 309–320

  11. Hillier B, Hanson J (1984) The social logic of space. Cambridge University Press, Cambridge

  12. Hyun KH, Min A, Kim S-J, Lee J-H (2016) Investigating cultural uniqueness in theme parks through finding relationships between visual integration of visitor traffics and capacity of service facilities. Int J Arch Comput 14:247–254

  13. Kloimüllner C, Papazek P, Hu B, Raidl GR (2014) Balancing bicycle sharing systems: an approach for the dynamic case. Eur Conf Evol Comput Comb Optim. Springer, Berlin, pp 73–84

  14. Koblin A (2009) Flight pattern. http://www.aaronkoblin.com/project/flight-patterns/. Accessed 1 Aug 2018

  15. Lee D, Offenhuber D, Duarte F, Biderman A, Ratti C (2018) Monitour: tracking global routes of electronic waste. Waste Manag 72:362–370

  16. Min DA, Hyun KH, Kim S-J, Lee J-H (2017) A rule-based servicescape design support system from the design patterns of theme parks. Adv Eng Inform 32:77–91

  17. O’Brien O (2013) Bike share map. http://bikes.oobrien.com/seoul/#zoom=15&lon=126.9935&lat=37.5621. Accessed 1 Aug 2018

  18. O’Brien O (2017) Social benefits from public bike share data. http://oobrien.com/2017/10/social-benefits-from-public-bike-share-data/. Accessed 1 Aug 2018

  19. Pal A, Zhang Y (2017) Free-floating bike sharing: solving real-life large-scale static rebalancing problems. Transp Res Part C Emerg Technol 80:92–116

  20. Pan L, Cai Q, Fang Z, Tang P, Huang L (2018) Rebalancing dockless bike sharing systems. https://arxiv.org/pdf/1802.04592.pdf. Accessed 23 Nov 2018

  21. Pei W, Wu Y, Wang S, Xiao L, Jiang H, Qayoom A (2018) BVis: urban traffic visual analysis based on bus sparse trajectories. J Vis 21:873–883

  22. Rainer-Harbach M, Papazek P, Hu B, Raidl GR (2013) Balancing bicycle sharing systems: a variable neighborhood search approach. In: European conference on evolutionary computation in combinatorial optimization. Springer, Berlin, pp 121–132

  23. Regue R, Recker W (2014) Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem. Transp Res Part E Logist Transp Rev 72:192–209

  24. Tang M (2018) From agent to avatar. In: Conference of the association for computer-aided architectural design research in Asia (CAADRIA 2018), Beijing, China. Tsinghua University, Beijing, pp 503–512

  25. Tang M, Hu Y (2017) Pedestrian simulation in transit stations using agent-based analysis. Urban Rail Transit 3:54–60

  26. Tufte E (2011) The visual display of quantitative information. Graphics Press, Cheshire

  27. Turner A, Penn A (1999) Making isovists syntactic: isovist integration analysis. In: 2nd International symposium on space syntax, Brasilia

  28. Turner A, Doxa M, O’Sullivan D, Penn A (2001) From isovists to visibility graphs: a methodology for the analysis of architectural space. Environ Plan B Plan Des 28:103–121

  29. Urbica (2016) City bike rebalanced. https://medium.com/@Urbica.co/city-bike-rebalanced-92ac61a867c7. Accessed 23 Nov 2018

  30. Xie C, Ma G, Li Q, Xun J, Dong J (2016) Visual exploration of tsunami evacuation planning. J Vis 19:475–487

  31. Yan Y, Tao Y, Xu J, Ren S, Lin H (2018) Visual analytics of bike-sharing data based on tensor factorization. J Vis 21:495–509

  32. Yuksel ME (2018) Agent-based evacuation modeling with multiple exits using neuroevolution of augmenting topologies. Adv Eng Inform 35:30–55

  33. Zhang L, Tang S, Yang Z, Hu J, Shu Y, Cheng P, Chen J (2016) Demo: data analysis and visualization in bike-sharing systems. In: Proceedings of the 14th annual international conference on mobile systems, applications, and services companion. ACM, pp 128–128

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP, Ministry of Science, ICT and Future Planning) (NRF-2017R1C1B5018240).

Author information

Correspondence to Kyung Hoon Hyun.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ban, S., Hyun, K.H. Curvature-based distribution algorithm: rebalancing bike sharing system with agent-based simulation. J Vis 22, 587–607 (2019). https://doi.org/10.1007/s12650-019-00557-6

Download citation

Keywords

  • Bicycle-sharing system
  • Agent-based simulation
  • Virtual environment
  • Rebalancing problem
  • Urban traffic
  • Rebalancing strategy