Pricing of Shared-Parking Lot: An Application of Hotelling Model

  • Wei Zhang
  • Shuaian WangEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 98)


Shared-parking lot brings utilization improvement, but also has its disadvantage compared with traditional parking lot while they are competing for public users. In the market including both shared-parking lot and traditional parking lot, parking lot operators need to know how to deal with parking price to be competitive in the market. The Hotelling model is applied in this paper to study the product differentiation of traditional parking lot and shared-parking lot, with some equilibrium analyses to figure out equilibrium parking prices of both parking lots while considering their competition in the market. Two points of indifferent consumers exist in the competition of the traditional parking lot and the shared- parking lot.



This research is sponsored by the National Natural Science Foundation of China (No. 71771050).


  1. Blunden, W.R.: The Land-Use/Transport System. Analysis and Synthesis (1971)Google Scholar
  2. Chou, S., Lin, S., Li, C.: Dynamic parking negotiation and guidance using an agent-based platform. Expert Syst. Appl. 35(3), 805–817 (2008)CrossRefGoogle Scholar
  3. Cleveland, D.E.: Accuracy of the periodic check parking study. Traffic Eng. 33(12), 14–17 (1963)Google Scholar
  4. Geng, Y., Cassandras, C.: New “smart parking” system based on resource allocation and reservations. IEEE Trans. Intell. Transp. Syst. 14(3), 1129–1139 (2013)CrossRefGoogle Scholar
  5. Liu, Z., Meng, Q.: Bus-based park-and-ride system: a stochastic model on multimodal network with congestion pricing schemes. Int. J. Syst. Sci. 45(5), 994–1006 (2014)MathSciNetCrossRefGoogle Scholar
  6. Liu, Z., Wang, S., Chen, W., Zheng, Y.: Willingness to board: a novel concept for modeling queuing up passengers. Transp. Res. Part B Methodol. 90, 70–82 (2016)CrossRefGoogle Scholar
  7. Liu, Z., Yan, Y., Qu, X., Zhang, Y.: Bus stop-skipping scheme with random travel time. Transp. Res. Part C Emerg. Technol. 35, 46–56 (2013)CrossRefGoogle Scholar
  8. Meng, Q., Qu, X.: Estimation of rear-end vehicle crash frequencies in urban road tunnels. Accid. Anal. Prev. 48, 254–263 (2012)CrossRefGoogle Scholar
  9. Qu, X., Zhang, J., Wang, S.: On the stochastic fundamental diagram for freeway traffic: model development, analytical properties, validation, and extensive applications. Transp. Res. Part B Methodol. 104, 256–271 (2017)CrossRefGoogle Scholar
  10. Richardson, A.J.: An improved parking duration study method. In: Proceedings of the 7th Australian Road Research Board Conference, Adelaide, Australia, pp. 397–413 (1974)Google Scholar
  11. Shao, C., Yang, H., Zhang, Y., Ke, J.: A simple reservation and allocation model of shared parking lots. Transp. Res. Part C Emerg. Technol. 71, 303–312 (2016)CrossRefGoogle Scholar
  12. Small, K.A.: The scheduling of consumer activities: work trips. Am. Econ. Rev. 72(3), 467–479 (1982)Google Scholar
  13. Teodorovic, D., Lucic, P.: Intelligent parking systems. Eur. J. Oper. Res. 175(3), 1666–1681 (2006)CrossRefGoogle Scholar
  14. Zhou, M., Qu, X., Li, X.: A recurrent neural network based microscopic car following model to predict traffic oscillation. Transp. Res. Part C Emerg. Technol. 84, 245–264 (2017)CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Logistics and Maritime StudiesThe Hong Kong Polytechnic UniversityKowloonHong Kong
  2. 2.The Hong Kong Polytechnic University Shenzhen Research InstituteShenzhenChina

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