Recognition and Recommendation of Parking Places

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8864)


Current solutions to recommend available parking spaces rely on options like: intentional user feedback; installing data collectors in volunteering fleet vehicles, or; installing static sensors to monitor available parking spaces. In this paper we propose a solution based application that runs on commodity smartphones and makes use of the advanced sensor capabilities in these devices, along with methods of statistical analysis of the collected sensor data to provide useful recommendations. We exploit a combination of \(k\)-medoid clustering and Conditional Random Fields to reliably detect a user parking with a limited sensor capability. Next, we outline a method based on Markov Chains to calculate the probability of finding a parking space near a given location. We also enhance the solution with more sensor capability to discover desirable properties in parking spaces.


Hide Markov Model Parking Place Dynamic Time Warping Conditional Random Field Parking Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proceedings of the VLDB Endowment 1(2), 1542–1552 (2008)CrossRefGoogle Scholar
  2. 2.
    Elliott, R.J., Aggoun, L., Moore, J.B.: Hidden Markov Models. Springer, Heidelberg (1995)zbMATHGoogle Scholar
  3. 3.
    Gallivan, S.D.: IBM global parking survey: Drivers share worldwide parking woes. Technical report, IBM (2011)Google Scholar
  4. 4.
    Hildenbrand, J.: Google releases open spot for android – find and share parking (July 10, 2010) (retrieved June 24, 2013)
  5. 5.
    Kaufman, L., Rousseeuw, P.: Clustering by means of medoids. In: Statistical Data Analysis Based on the L1 Norm and Related Methods, pp. 405–416. Springer, Amsterdam (1987)Google Scholar
  6. 6.
    Koster, A., Koch, F., Bazzan, A.L.C.: Incentivising crowdsourced parking solutions. In: Nin, J., Villatoro, D. (eds.) CitiSens 2013. LNCS(LNAI), vol. 8313, pp. 36–43. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  7. 7.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12(2), 74–82 (2010)CrossRefGoogle Scholar
  8. 8.
    Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML, pp. 282–289. Morgan Kaufmann, San Francisco (2001)Google Scholar
  9. 9.
    Mathur, S., Tong, J., Kasturirangan, N., Chandrasekaran, J., Xue, W., Gruteser, M., Trappe, W.: ParkNet: drive-by sensing of road-side parking statistics. In: Proc. of MobiSys 2010, pp. 123–136. ACM (2010)Google Scholar
  10. 10.
    Park, W.J., Kim, B.S., Kim, D.S., Lee, K.H.: Parking space detection using ultrasonic sensor in parking assistance system. In: Proc. of the IEEE Intelligent VEhicles Symposium, pp. 1039–1044. IEEE (2008)Google Scholar
  11. 11.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 26(1), 43–49 (1978)CrossRefzbMATHGoogle Scholar
  12. 12.
    Sherwin, I.: Google Labs’ Open Spot: A useful application that no one uses (July 10, 2011) (retrieved May 15, 2014)
  13. 13.
    Srikanth, S., Pramod, P.J., Dileep, K.P., Tapas, S., Patil, M.U., Sarat, C.B.N.: Design and implementation of a prototype smart PARKing (SPARK) system using wireless sensor networks. In: Proceedings of the Advanced Information Networking and Applications Workshops (WAINA 2009), pp. 401–406 (2009)Google Scholar
  14. 14.
    Stenneth, L., Wolfson, O., Xu, B., Yu, P.S.: Transportation mode detection using mobile phones and GIS information. In: Proc. of the 19th ACM SIGSPATIAL International Converence on Advances in Geographic Information Systems, pp. 54–63. ACM (2011)Google Scholar
  15. 15.
    Stenneth, L., Wolfson, O., Xu, B., Yu, P.S.: PhonePark: Street parking using mobile phones. In: Proceedings of the 13th IEEE International Conference on Mobile Data Management (MDM 2012), pp. 278–279. IEEE (2012)Google Scholar
  16. 16.
    Suhr, J.K., Jung, H.G., Bae, K., Kim, J.: Automatic free parking space detection by using motion stereo-based 3D reconstruction. Machine Vision and Applicatons 21(2), 163–176 (2010)CrossRefGoogle Scholar
  17. 17.
    Sutton, R.S., Barto, A.: Reinforcement Learning: An Introduction. MIT Press (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Samsung Research InstituteCampinasBrazil

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