What Is the Impact of On-street Parking Information for Drivers?

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11474)


Parking Guidance and Information (PGI) solutions are a well-known class of Intelligent Transportation Systems meant to support drivers by recommending locations and routes with higher chance to find parking. However, the relevance of such systems for on-street parking spaces is barely studied. In this paper, we investigate the consequences of providing the drivers with different parking information to the search. Based on real-world parking data from San Francisco, we investigated the scenario in which a driver does not find a parking space at the destination and has to decide on the next road to go. We consider three different scenarios: (I) No parking availability information; (II) static information about the capacity of a road segment and temporary parking limitations; (III) real-time information collected from stationary sensors. Clearly the latter has strong implications in terms of deployment and operational costs. These scenarios lead to three different guidance strategies for a PGI system. The empirical experiments we conducted on real on-street parking data from San Francisco show that there is a significant reduction of parking search with more informed strategies, and that the use of real-time information offers only a limited improvement over static one.



This research has been supported by the German Research Foundation (DFG) through the Research Training Group SocialCars (GRK 1931). The focus of the SocialCars Research Training Group is on significantly improving the city’s future road traffic, through cooperative approaches. This support is gratefully acknowledged.


  1. 1.
    Shoup, D.C.: Cruising for parking. Transp. Policy 13(6), 479–486 (2006)CrossRefGoogle Scholar
  2. 2.
    Lin, T., Rivano, H., Mouël, F.L.: A survey of smart parking solutions. IEEE Trans. Intell. Transp. Syst. PP(99), 1–25 (2017)Google Scholar
  3. 3.
    Ma, S., Wolfson, O., Xu, B.: UPDetector: sensing parking/unparking activities using smartphones. In: Proceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science, pp. 76–85. ACM (2014)Google Scholar
  4. 4.
    Teodorović, D., Lučić, P.: Intelligent parking systems. Eur. J. Oper. Res. 175(3), 1666–1681 (2006)CrossRefGoogle Scholar
  5. 5.
    Axhausen, K., Polak, J., Boltze, M., Puzicha, J.: Effectiveness of the parking guidance information system in Frankfurt am Main. Traffic Eng.+ Control 35(5), 304–309 (1994)Google Scholar
  6. 6.
    Xu, B., Wolfson, O., Yang, J., Stenneth, L., Yu, P.S., Nelson, P.C.: Real-time street parking availability estimation. In: 2013 IEEE 14th International Conference on Mobile Data Management (MDM), vol. 1. IEEE, pp. 16–25 (2013)Google Scholar
  7. 7.
    Bock, F., Di Martino, S.: How many probe vehicles do we need to collect on-street parking information? In: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 538–543. IEEE (2017)Google Scholar
  8. 8.
    Bock, F., Liu, J., Sester, M.: Learning on-street parking maps from position information of parked vehicles. In: Sarjakoski, T., Santos, M.Y., Sarjakoski, L.T. (eds.) Geospatial Data in a Changing World. LNGC, pp. 297–314. Springer, Cham (2016). Scholar
  9. 9.
    Coric, V., Gruteser, M.: Crowdsensing maps of on-street parking spaces. In: 2013 IEEE International Conference on Distributed Computing in Sensor Systems, pp. 115–122. IEEE, May 2013.
  10. 10.
    SFMTA: SFpark: putting theory into practice. Pilot project summary and lessons learned (2014). Accessed 24 June 2016
  11. 11.
    Kotb, A.O., Shen, Y., Huang, Y.: Smart parking guidance, monitoring and reservations: a review. IEEE Intell. Transp. Syst. Mag. 9(2), 6–16 (2017)CrossRefGoogle Scholar
  12. 12.
    Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49(11), 32–39 (2011)CrossRefGoogle Scholar
  13. 13.
    Rinne, M., Törmä, S., Kratinov, D.: Mobile crowdsensing of parking space using geofencing and activity recognition. In: 10th ITS European Congress, Finland, Helsinki, pp. 16–19 (2014)Google Scholar
  14. 14.
    Mathur, S., et al.: ParkNet: drive-by sensing of road-side parking statistics. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp. 123–136. ACM, New York (2010)Google Scholar
  15. 15.
    Bock, F., Di Martino, S., Sester, M.: What are the potentialities of crowdsourcing for dynamic maps of on-street parking spaces? In: Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2016, pp. 19–24. ACM, New York (2016).
  16. 16.
    Richter, F., Di Martino, S., Mattfeld, D.C.: Temporal and spatial clustering for a parking prediction service. In: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 278–282. IEEE (2014)Google Scholar
  17. 17.
    Tasseron, G., Martens, K., van der Heijden, R.: The potential impact of vehicle-to-vehicle communication on on-street parking under heterogeneous conditions. IEEE Intell. Transp. Syst. Mag. 8(2), 33–42 (2016)CrossRefGoogle Scholar
  18. 18.
    Waterson, B., Hounsell, N., Chatterjee, K.: Quantifying the potential savings in travel time resulting from parking guidance systems—a simulation case study. J. Oper. Res. Soc. 52(10), 1067–1077 (2001)CrossRefGoogle Scholar
  19. 19.
    Benenson, I., Martens, K., Birfir, S.: PARKAGENT: an agent-based model of parking in the city. Comput. Environ. Urban Syst. 32(6), 431–439 (2008). Scholar
  20. 20.
    Millard-Ball, A., Weinberger, R.R., Hampshire, R.C.: Is the curb 80% full or 20% empty? Assessing the impacts of San Francisco’s parking pricing experiment. Transp. Res. Part A: Policy Pract. 63, 76–92 (2014). Scholar
  21. 21.
    Bonsall, P., Palmer, I.: Modelling drivers’ car parking behaviour using data from a travel choice simulator. Transp. Res. Part C: Emerg. Technol. 12(5), 321–347 (2004)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Cartography and GeoinformaticsLeibniz UniversityHannoverGermany
  2. 2.Department of Electrical and Telecommunications EngineeringUniversity of Naples Federico IINaplesItaly

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