Skip to main content
SpringerLink
Log in

Parking Recommender System Using Q-Learning and Cloud Computing

  • Conference paper
  • First Online:
Cyber Security and Computer Science (ICONCS 2020)

Included in the following conference series:

Abstract

Artificial Intelligence (AI) based recommender systems help to make our life easy and comfortable. From simple chatbot to YouTube recommendation, AI is used to recommend news, videos, etc. which provide us more information and saves our time. In big cities, parking seems to be a major problem where commuters need to find a suitable parking space among many parking areas which cause wastage of time and fuel. Our paper proposes a parking recommender system where commuters will be suggested a parking area to a nearby place for helping them to save time, parking cost and ensure high security. To collect data of parking spaces, we propose a Cloud architecture where we use the concept of Edge and Cloud computing to collect and process data smoothly and reduce latency. To deal with bigger amounts of data we use Data Streaming Pipelining to process and analyze those data. We use Amazon Web Services (AWS) to implement our proposed Cloud architecture. For creating the AI based recommender system, we propose the Q-learning algorithm with \({\varepsilon }\)-soft policy to suggest nearby parking areas. Our novel approach will be helpful for both local and global citizens to find an ideal parking area close to their working place, home, etc. Our proposed Cloud architecture is able to reduce latency and make data transferring system faster. Also the Q-learning algorithm can outperform in terms of both certain and uncertain situations.

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 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
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

References

  1. Dong, S., Chen, M., Peng, L., Li, H.: Parking rank: a novel method of parking lots sorting and recommendation based on public information. In: 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, pp. 1381–1386. IEEE Press (2018). https://doi.org/10.1109/ICIT.2018.8352381

  2. Fang, J., Ma, A., Fan, H., Cai, M., Song, S.: Research on smart parking guidance and parking recommendation algorithm. In: 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, pp. 209–212. IEEE Press (2017). https://doi.org/10.1109/ICSESS.2017.8342898

  3. Srisura, B., Wan, C., Sae-lim, D., Meechoosup, P., Mar Win, P.: User preference recommendation on mobile car parking application. In: 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), Bamberg, pp. 59–64. IEEE Press (2018). https://doi.org/10.1109/MobileCloud.2018.00017

  4. Yavari, A., Jayaraman, P.P., Georgakopoulos, D.: Contextualised service delivery in the internet of things: parking recommender for smart cities. In: IEEE 3rd World Forum on Internet of Things (WF-IoT), Reston, VA, pp. 454–459. IEEE Press (2016). https://doi.org/10.1109/WF-IoT.2016.7845479

  5. Srisura, B., Avatchanakorn, V.: Periodical mobile recommendation toward parking conflict reduction. In: IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA), Tokyo, Japan, pp. 397–402. IEEE Press (2019). https://doi.org/10.1109/IEA.2019.8715025

  6. Yuan, N.J., Zheng, Y., Zhang, L., Xie, X.: T-finder: a recommender system for finding passengers and vacant taxis. IEEE Trans. Knowl. Data Eng. 25, 2390–2403 (2013). https://doi.org/10.1109/TKDE.2012.153

    Article  Google Scholar 

  7. Fu, J., Chen, Z., Sun, R., Yang, B.: Reservation based optimal parking loT recommendation model in internet of vehicle environment. China Commun. 11, 38–48 (2014). https://doi.org/10.1109/CC.2014.6969792

    Article  Google Scholar 

  8. Koster, A., Oliveira, A., Volpato, O., Delvequio, V., Koch, F.: Recognition and recommendation of parking places. In: Bazzan, A.L.C., Pichara, K. (eds.) IBERAMIA 2014. LNCS (LNAI), vol. 8864, pp. 675–685. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12027-0_54

    Chapter  Google Scholar 

  9. Hasan, M.O., Islam, M.M., Alsaawy, Y.: Smart parking model based on internet of things (IoT) and TensorFlow. In: 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia, pp. 1–5. IEEE Press (2019). https://doi.org/10.1109/ICSCC.2019.8843651

  10. Shinde, S., Patil, A., Chavan, S., Deshmukh, S., Ingleshwar, S.: IoT based parking system using Google. In: International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palldam, pp. 634–636. IEEE Press (2017). https://doi.org/10.1109/I-SMAC.2017.8058256

  11. Rummery, G.A., Niranjan, M.: On-line Q-learning using connectionist systems. Department of Engineering, University of Cambridge (1994)

    Google Scholar 

  12. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  13. Chi, C.Y., Tsai, R.T.H., Lai, J.Y., Hsu, J.Y.J.: A reinforcement learning approach to emotion-based automatic playlist generation. In: 2010 International Conference on Technologies and Applications of Artificial Intelligence, pp. 60–65. IEEE (2010)

    Google Scholar 

  14. Konstan, J.A., Riedl, J., Borchers, A., Herlocker, J.L.: Recommender systems: a grouplens perspective. In: Recommender Systems: Papers from the 1998 Workshop (AAAI Technical Report WS-98-08), pp. 60–64 (1998)

    Google Scholar 

  15. Wu, T., Tsai, P., Hu, N., Chen, J.: Research and implementation of auto parking system based on ultrasonic sensors. In: International Conference on Advanced Materials for Science and Engineering (ICAMSE), Tainan, pp. 643–645. IEEE Press (2016). https://doi.org/10.1109/ICAMSE.2016.7840267

  16. Shao, Y., Chen, P., Cao, T.: A grid projection method based on ultrasonic sensor for parking space detection. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, pp. 3378–3381. IEEE Press (2018). https://doi.org/10.1109/IGARSS.2018.8519022

  17. Sutton, R.S., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  18. Islam, M.M., Khan, Z., Alsaawy, Y.: An implementation of harmonizing internet of things (IoT) in cloud. In: Pathan, A.-S.K., Fadlullah, Z.M., Guerroumi, M. (eds.) SGIoT 2018. LNICST, vol. 256, pp. 3–12. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05928-6_1

    Chapter  Google Scholar 

  19. Taghipour, N., Kardan, A., Ghidary, S.S.: Usage-based web recommendations: a reinforcement learning approach. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 113–120. ACM (2007)

    Google Scholar 

  20. Bu, X., Rao, J., Xu, C.: Coordinated self-configuration of virtual machines and appliances using a model-free learning approach. IEEE Trans. Parallel Distrib. Syst. 24, 681–690 (2013). https://doi.org/10.1109/TPDS.2012.174

    Article  Google Scholar 

  21. Oshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In: ISMIR, vol. 6, p. 7 (2006)

    Google Scholar 

  22. Song, Y., Dixon, S., Pearce, M.: A survey of music recommendation systems and future perspectives. In: 9th International Symposium on Computer Music Modeling and Retrieval (2012)

    Google Scholar 

  23. Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6(Sep), 1265–1295 (2005)

    MathSciNet  MATH  Google Scholar 

  24. Suhr, J.K., Jung, H.G., Bae, K., Kim, J.: Automatic free parking space detection by using motion stereo-based 3D reconstruction. Mach. Vis. Appl. 21(2), 163–176 (2010)

    Article  Google Scholar 

  25. Elliott, R.J., Aggoun, L., Moore, J.B.: Hidden Markov Models. Springer, Heidelberg (1995). https://doi.org/10.1007/978-0-387-84854-9

    Book  MATH  Google Scholar 

  26. Amazon Homepage. https://aws.amazon.com

  27. Towards Data Science. https://towardsdatascience.com

Download references

Author information

Authors and Affiliations

  1. BRAC University, Dhaka, Bangladesh

    Md. Omar Hasan & Khandakar Razoan Ahmed

  2. United International University, Dhaka, Bangladesh

    Md. Motaharul Islam

Authors
  1. Md. Omar Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khandakar Razoan Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Md. Motaharul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md. Motaharul Islam .

Editor information

Editors and Affiliations

  1. Daffodil International University, Dhaka, Bangladesh

    Touhid Bhuiyan

  2. Daffodil International University, Dhaka, Bangladesh

    Md. Mostafijur Rahman

  3. Daffodil International University, Dhaka, Bangladesh

    Md. Asraf Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hasan, M.O., Ahmed, K.R., Islam, M.M. (2020). Parking Recommender System Using Q-Learning and Cloud Computing. In: Bhuiyan, T., Rahman, M.M., Ali, M.A. (eds) Cyber Security and Computer Science. ICONCS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-52856-0_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-52856-0_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-52855-3

  • Online ISBN: 978-3-030-52856-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation