Skip to main content

A Novel Approach for Solving Large-Scale Bike Sharing Station Planning Problems

  • Conference paper
  • First Online:
Learning and Intelligent Optimization (LION 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11968))

Included in the following conference series:

  • 789 Accesses


In large cities all around the world, individual and motorized traffic is still prevalent. This circumstance compromises the quality of living, and moreover, space inside cities for parking individual vehicles for movement is scarce and is becoming even scarcer. Thus, the need for a greener means of transportation and less individual vehicles inside the cities is demanded and rising. An already accepted and established solution possibility to these problems are public bike sharing systems (PBS). Such systems are often freely available to people for commuting within the city and utilize the available space in the city more efficiently than individual vehicles. When building or extending a PBS, a certain optimization goal is to place stations inside a city or a part of it, such that the number of bike trips per time unit is maximized under certain budget constraints. In this context, it is also important to consider rebalancing and maintenance costs as they introduce substantial supplementary costs in addition to the fixed and variable costs when building or extending a PBS. In contrast to the literature, this work introduces a novel approach which is particularly designed to scale well to large real-world instances. Based on our previous work, we propose a multilevel refinement heuristic operating on hierarchically clustered input data. This way, the problem is coarsened until a manageable input size is reached, a solution is derived, and then step by step extended and refined until a valid solution for the whole original problem instance is obtained. As an enhancement to our previous work, we introduce the following extensions. Instead of considering an arbitrary integral number of slots for stations, we now use sets of predefined station configurations. Moreover, a local search is implemented as refinement step in the multilevel refinement heuristic and we now consider real-world input data for the city of Vienna.

We thank the LOGISTIKUM Steyr, the Austrian Institute of Technology, and Rosinak & Partner for the collaboration on this topic. This work is supported by the Austrian Research Promotion Agency (FFG) under contract 849028.

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

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. 1.

  2. 2.


  1. Biesinger, B., Hu, B., Stubenschrott, M., Ritzinger, U., Prandtstetter, M.: Optimizing charging station locations for electric car-sharing systems. In: Hu, B., López-Ibáñez, M. (eds.) EvoCOP 2017. LNCS, vol. 10197, pp. 157–172. Springer, Cham (2017).

    Chapter  Google Scholar 

  2. Contreras, I.: Hub location problems. In: Laporte, G., Nickel, S., da Gama, F.S. (eds.) Location Science, pp. 311–344. Springer, Cham (2015).

    Chapter  Google Scholar 

  3. DeMaio, P.: Bike-sharing: history, impacts, models of provision, and future. Public Transp. 12(4), 41–56 (2009)

    Article  Google Scholar 

  4. Farahani, R.Z., Hekmatfar, M., Arabani, A.B., Nikbakhsh, E.: Hub location problems: a review of models, classification, solution techniques, and applications. CAIE 64(4), 1096–1109 (2013)

    Google Scholar 

  5. Gavalas, D., Konstantopoulos, C., Pantziou, G.: Design and management of vehicle sharing systems: A survey of algorithmic approaches. In: Obaidat, M.S., Nicopolitidis, P. (eds.) Smart Cities and Homes: Key Enabling Technologies, pp. 261–289. Elsevier Science (2016)

    Google Scholar 

  6. Hu, S.R., Liu, C.T.: An optimal location model for a bicycle sharing program with truck dispatching consideration. In: IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pp. 1775–1780. IEEE (2014)

    Google Scholar 

  7. Kloimüllner, C., Papazek, P., Hu, B., Raidl, G.R.: Balancing bicycle sharing systems: an approach for the dynamic case. In: Blum, C., Ochoa, G. (eds.) EvoCOP 2014. LNCS, vol. 8600, pp. 73–84. Springer, Heidelberg (2014).

    Chapter  Google Scholar 

  8. Kloimüllner, C., Raidl, G.R.: Full-load route planning for balancing bike sharing systems by logic-based benders decomposition. Networks 69(3), 270–289 (2017)

    Article  MathSciNet  Google Scholar 

  9. Kloimüllner, C., Raidl, G.R.: Hierarchical clustering and multilevel refinement for the bike-sharing station planning problem. In: Battiti, R., Kvasov, D.E., Sergeyev, Y.D. (eds.) LION 2017. LNCS, vol. 10556, pp. 150–165. Springer, Cham (2017).

    Chapter  Google Scholar 

  10. Lin, J.R., Yang, T.H.: Strategic design of public bicycle sharing systems with service level constraints. Transp. Res. E-Log. 47(2), 284–294 (2011)

    Article  Google Scholar 

  11. Lin, J.R., Yang, T.H., Chang, Y.C.: A hub location inventory model for bicycle sharing system design: formulation and solution. CAIE 65(1), 77–86 (2013)

    Google Scholar 

  12. Martinez, L.M., Caetano, L., Eiró, T., Cruz, F.: An optimisation algorithm to establish the location of stations of a mixed fleet biking system: an application to the city of Lisbon. Procedia Soc. Behav. Sci. 54, 513–524 (2012)

    Article  Google Scholar 

  13. Rainer-Harbach, M., Papazek, P., Hu, B., Raidl, G.R., Kloimüllner, C.: PILOT, GRASP, and VNS approaches for the static balancing of bicycle sharing systems. JOGO 63(3), 597–629 (2015)

    MathSciNet  MATH  Google Scholar 

  14. ReVelle, C.S., Eiselt, H.A.: Location analysis: a synthesis and survey. EJOR 165(1), 1–19 (2005)

    Article  MathSciNet  Google Scholar 

  15. Saharidis, G., Fragkogios, A., Zygouri, E.: A multi-periodic optimization modeling approach for the establishment of a bike sharing network: a case study of the city of Athens. In: Proceedings of the International MultiConference of Engineers and Computer Scientists 2014, vol. II, No. 2210, pp. 1226–1231. LNECS. Newswood Limited (2014)

    Google Scholar 

  16. Straub, M., et al.: Semi-automated location planning for urban bike-sharing systems. In: Proceedings of the 7th Transport Research Arena (TRA 2018), pp. 1–10, Vienna, Austria (2018)

    Google Scholar 

  17. Walshaw, C.: A multilevel approach to the travelling salesman problem. Oper. Res. 50(5), 862–877 (2002)

    Article  MathSciNet  Google Scholar 

  18. Walshaw, C.: Multilevel refinement for combinatorial optimisation problems. Ann. Oper. Res. 131(1), 325–372 (2004)

    Article  MathSciNet  Google Scholar 

  19. Yang, T.H., Lin, J.R., Chang, Y.C.: Strategic design of public bicycle sharing systems incorporating with bicycle stocks considerations. In: 40th International Conference on Computers and Industrial Engineering (CIE), pp. 1–6. IEEE (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Christian Kloimüllner .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kloimüllner, C., Raidl, G.R. (2020). A Novel Approach for Solving Large-Scale Bike Sharing Station Planning Problems. In: Matsatsinis, N., Marinakis, Y., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2019. Lecture Notes in Computer Science(), vol 11968. Springer, Cham.

Download citation

Publish with us

Policies and ethics