Skip to main content
SpringerLink
Log in

Dynamic Pricing for Parking Facility

  • Conference paper
  • First Online:
Advances in Intelligent Networking and Collaborative Systems (INCoS 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 182))

  • 263 Accesses

Abstract

Urbanization benefits residents of urban cities and the modern society. However, public resources—such as parking facility—can be limited. A solution is to make good use of dynamic pricing, which can help adjust the available resources. For instance, dynamic pricing for parking facility helps maximize parking resource utilization and optimize the parking revenue. In this paper, we present a dynamic pricing solution for parking facility. It utilizes available public resources and optimizes revenue with predefined restrictions. This solution that adapts reinforcement learning in predicting pricing. It also handles price restrictions. Evaluation results show the effectiveness and practicality of our solution in dynamic pricing for parking facility.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
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

References

  1. Bo, D., Ai, L., Chen, Y.: Research and application of big data correlation analysis in education. In: Barolli, L., Nishino, H., Miwa, H. (eds.) INCoS 2019. AISC, vol. 1035, pp. 454–462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29035-1_44

    Chapter  Google Scholar 

  2. Cuzzocrea, A., et al.: The emerging challenges of big data lakes, and a real-life framework for representing, managing and supporting machine learning on big Arctic data. In: Barolli, L., Miwa, H. (eds.) INCoS 2022. LNNS, vol. 527, pp. 161–174. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-14627-5_16

  3. Leung, C.K., et al.: Big data visualization and visual analytics of COVID-19 data. In: IV 2020, pp. 415–420 (2020)

    Google Scholar 

  4. Anderson-Grégoire, I.M., et al.: A big data science solution for analytics on moving objects. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021, vol. 2. LNNS, vol. 226, pp. 133–145. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75075-6_11

    Chapter  Google Scholar 

  5. Dierckens, K.E., et al.: A data science and engineering solution for fast k-means clustering of big data. In: IEEE TrustCom-BigDataSE-ICESS 2017, pp. 925–932 (2017)

    Google Scholar 

  6. Alam, M.T., Ahmed, C.F., Samiullah, M., Leung, C.K.: Discriminating frequent pattern based supervised graph embedding for classification. In: Karlapalem, K., et al. (eds.) PAKDD 2021, Part II. LNCS (LNAI), vol. 12713, pp. 16–28. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75765-6_2

    Chapter  Google Scholar 

  7. Alam, M.T., Ahmed, C.F., Samiullah, M., Leung, C.K.: Mining frequent patterns from hypergraph databases. In: Karlapalem, K., et al. (eds.) PAKDD 2021, Part II. LNCS (LNAI), vol. 12713, pp. 3–15. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75765-6_1

    Chapter  Google Scholar 

  8. Chowdhury, M.E.S., et al.: A new approach for mining correlated frequent subgraphs. ACM TMIS 13(1), 9:1–9:28 (2022)

    Google Scholar 

  9. Leung, C.K., Jiang, F.: Frequent itemset mining of uncertain data streams using the damped window model. In: ACM SAC 2011, pp. 950–955 (2011)

    Google Scholar 

  10. Leung, C.K.: Mining uncertain data. WIRES Data Min. Knowl. Discov. 1(4), 316–329 (2011)

    Article  Google Scholar 

  11. Roy, K.K., Moon, M.H.H., Rahman, M.M., Ahmed, C.F., Leung, C.K.: Mining sequential patterns in uncertain databases using hierarchical index structure. In: Karlapalem, K., et al. (eds.) PAKDD 2021, Part II. LNCS (LNAI), vol. 12713, pp. 29–41. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75765-6_3

    Chapter  Google Scholar 

  12. Froese, R., et al.: The border k-means clustering algorithm for one dimensional data. In: IEEE BigComp 2022, pp. 35–42 (2022)

    Google Scholar 

  13. Leung, C.K., et al.: Personalized DeepInf: enhanced social influence prediction with deep learning and transfer learning. In: IEEE BigData 2019, pp. 2871–2880 (2019)

    Google Scholar 

  14. Leung, C.K., et al.: Machine learning and OLAP on big COVID-19 data. In: IEEE BigData 2020, pp. 5118–5127 (2020)

    Google Scholar 

  15. Madill, E., et al.: ScaleSFL: a sharding solution for blockchain-based federated learning. In: ACM BSCI 2022, pp. 95–106 (2022)

    Google Scholar 

  16. Olawoyin, A.M., et al.: Big data management for machine learning from big data. In: Barolli, L. (ed.) AINA 2023, vol. 1. LNNS, vol. 661, pp. 393–405. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-29056-5_35

  17. de Guia, J., et al.: DeepGx: deep learning using gene expression for cancer classification. In: IEEE/ACM ASONAM 2019, pp. 913–920 (2019)

    Google Scholar 

  18. Fung, D.L.X., et al.: Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19. BMC J. Transl. Med. 19, 318:1–318:18 (2021)

    Google Scholar 

  19. Souza, J., Leung, C.K., Cuzzocrea, A.: An innovative big data predictive analytics framework over hybrid big data sources with an application for disease analytics. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) AINA 2020. AISC, vol. 1151, pp. 669–680. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44041-1_59

    Chapter  Google Scholar 

  20. Leung, C.K., Kaufmann, T.N., Wen, Y., Zhao, C., Zheng, H.: Revealing COVID-19 data by data mining and visualization. In: Barolli, L., Chen, H.-C., Miwa, H. (eds.) INCoS 2021. LNNS, vol. 312, pp. 70–83. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-84910-8_8

    Chapter  Google Scholar 

  21. Leung, C.K., et al.: Smart data analytics on COVID-19 data. In: IEEE iThings-GreenCom-CPSCom-SmartData-Cybermatics 2021, pp. 372–379 (2021)

    Google Scholar 

  22. Leung, C.K., Zhao, C.: Big data intelligence solution for health analytics of COVID-19 data with spatial hierarchy. In: IEEE DataCom 2021, pp. 13–20 (2021)

    Google Scholar 

  23. Anuraj, A., et al.: Sports data mining for cricket match prediction. In: Barolli, L. (ed.) AINA 2023, vol. 3. LNNS, vol. 655, pp. 668–680. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28694-0_63

  24. Isichei, B.C., et al.: Sports data management, mining, and visualization. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022, vol. 2. LNNS, vol. 450, pp. 141–153. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99587-4_13

    Chapter  Google Scholar 

  25. Balbin, P.P.F., et al.: Predictive analytics on open big data for supporting smart transportation services. Proc. Comput. Sci. 176, 3009–3018 (2020)

    Article  Google Scholar 

  26. Kolisnyk, M., et al.: Analysis of multi-dimensional road accident data for disaster management in smart cities. In: IEEE IRI 2022, pp. 43–48 (2022)

    Google Scholar 

  27. Leung, C.K., et al.: Data mining on open public transit data for transportation analytics during pre-COVID-19 era and COVID-19 era. In: Barolli, L., Li, K.F., Miwa, H. (eds.) INCoS 2020. AISC, vol. 1263, pp. 133–144. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-57796-4_13

    Chapter  Google Scholar 

  28. Leung, C.K., Braun, P., Hoi, C.S.H., Souza, J., Cuzzocrea, A.: Urban analytics of big transportation data for supporting smart cities. In: Ordonez, C., Song, I.-Y., Anderst-Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DaWaK 2019. LNCS, vol. 11708, pp. 24–33. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27520-4_3

    Chapter  Google Scholar 

  29. Cabusas, R.M., Epp, B.N., Gouge, J.M., Kaufmann, T.N., Leung, C.K., Tully, J.R.A.: Mining for fake news. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022, vol. 2. LNNS, vol. 450, pp. 154–166. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99587-4_14

    Chapter  Google Scholar 

  30. Choudhery, D., Leung, C.K.: Social media mining: prediction of box office revenue. In: IDEAS 2017, pp. 20–29 (2017)

    Google Scholar 

  31. Leung, C.K., Jiang, F., Poon, T.W., Crevier, P.-É.: Big data analytics of social network data: who cares most about you on Facebook? In: Moshirpour, M., Far, B., Alhajj, R. (eds.) Highlighting the Importance of Big Data Management and Analysis for Various Applications. SBD, vol. 27, pp. 1–15. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-60255-4_1

    Chapter  Google Scholar 

  32. Tanbeer, S.K., et al.: Interactive mining of strong friends from social networks and its applications in e-commerce. J. Organ. Comput. Electron. 24(2–3), 157–173 (2014)

    Article  Google Scholar 

  33. Arellano-Verdejo, J., Alba, E.: Optimal allocation of public parking slots using evolutionary algorithms. In: INCoS 2016, pp. 222–228 (2016)

    Google Scholar 

  34. de Almeida, P.R.L., et al.: A systematic review on computer vision-based parking lot management applied on public datasets. ESWA 198, 116731:1–116731:18 (2022)

    Google Scholar 

  35. Deng, D.: Dynamic pricing for predictive analytics in parking. M.Sc. thesis, University of Manitoba, Canada (2021)

    Google Scholar 

  36. Deng, D., et al.: Data analytics for parking facility management. In: Barolli, L., Miwa, H. (eds.) INCoS 2022. LNNS, vol. 527, pp. 117–129. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-14627-5_12

  37. Fan, J., et al.: Predicting vacant parking space availability: a long short-term memory approach. IEEE Intell. Transp. Syst. 14(2), 129–143 (2022)

    Article  MathSciNet  Google Scholar 

  38. Wu, Y., et al.: Competitive spatial pricing for urban parking systems: network structures and asymmetric information. IISE Trans. 54(2), 186–197 (2022)

    Google Scholar 

  39. Zeng, C., et al.: Parking occupancy prediction method based on multi factors and stacked GRU-LSTM. IEEE Access 10, 47361–47370 (2022)

    Article  Google Scholar 

  40. Zou, B., et al.: A mechanism design based approach to solving parking slot assignment in the information era. Transp. Res. Part B: Methodol. 81, 631–653 (2015)

    Article  Google Scholar 

  41. Sheelarani, S.P., et al.: Effective car parking reservation system based on Internet of Things technologies. In: StartUp Conclave 2016 (2016)

    Google Scholar 

  42. Du, Y., et al.: Allocation of street parking facilities in a capacitated network with equilibrium constraints on drivers’ traveling and cruising for parking. Transp. Res. Part C: Emerg. Technol. 101, 181–207 (2019)

    Article  Google Scholar 

  43. Inci, E., Lindsey, R.: Garage and curbside parking competition with search congestion. Reg. Sci. Urban Econ. 54, 49–59 (2015)

    Article  Google Scholar 

  44. Zhang, R., Zhu, L.: Curbside parking pricing in a city centre using a threshold. Transp. Policy 52, 16–27 (2016)

    Article  Google Scholar 

  45. Shoup, D.: The High Cost of Free Parking, Updated Routledge, Abingdon (2011)

    Google Scholar 

  46. Netessine, S., Shumsky, R.: Introduction to the theory and practice of yield management. INFORMS Trans. Educ. (ITE) 3(1), 34–44 (2002)

    Article  Google Scholar 

  47. den Boer, A.V.: Dynamic pricing and learning: historical origins, current research, and new directions. Surv. Oper. Res. Manag. Sci. 20(1), 1–18 (2015)

    MathSciNet  Google Scholar 

  48. Ye, P., et al.: Customized regression model for Airbnb dynamic pricing. In: ACM KDD 2018, pp. 932–940 (2018)

    Google Scholar 

  49. Zheng, N., Geroliminis, N.: Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing. Transp. Res. Part B: Methodol. 83, 36–58 (2016)

    Article  Google Scholar 

  50. Mackowski, D., et al.: Parking space management via dynamic performance-based pricing. Transp. Res. Part C: Emerg. Technol. 59, 66–91 (2015)

    Article  Google Scholar 

  51. Simaan, M., Cruz, J.B.: On the Stackelberg strategy in nonzero-sum games. J. Optim. Theory Appl. 11(5), 533–555 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  52. Lei, C., Ouyang, Y.: Dynamic pricing and reservation for intelligent urban parking management. Transp. Res. Part C: Emerg. Technol. 77, 226–244 (2017)

    Article  Google Scholar 

  53. Sammut, C., Webb, G.I (eds.): Bellman Equation, vol. 97. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8_71

  54. Kara, A., Dogan, I.: Reinforcement learning approaches for specifying ordering policies of perishable inventory systems. ESWA 91, 150–158 (2018)

    Google Scholar 

  55. Lu, R., et al.: A dynamic pricing demand response algorithm for smart grid: reinforcement learning approach. Appl. Energy 220, 220–230 (2018)

    Article  Google Scholar 

  56. Mocanu, E., et al.: On-line building energy optimization using deep reinforcement learning. IEEE Trans. Smart Grid 10(4), 3698–3708 (2019)

    Article  Google Scholar 

  57. van Hasselt, H., et al.: Deep reinforcement learning with double Q-learning. In: AAAI 2016, pp. 2094–2100 (2016)

    Google Scholar 

Download references

Acknowledgements

This work is partially supported by Mitacs, NSERC (Canada), University of Manitoba, and Winnipeg Airports Authority (WAA). Also thanks S. Marohn, C. McFadyen, R. Olaes-Zimolag, B. Podaima, T. Strome, R. Wei, and B. Zamorano for their domain expertise.

Author information

Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Deyu Deng, Carson K. Leung & Adam G. M. Pazdor

Authors
  1. Deyu Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carson K. Leung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adam G. M. Pazdor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carson K. Leung .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deng, D., Leung, C.K., Pazdor, A.G.M. (2023). Dynamic Pricing for Parking Facility. In: Barolli, L. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-031-40971-4_13

Download citation

Publish with us

Policies and ethics

Search

Navigation