Abstract
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.
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Notes
- 1.
In the following we will identify with the term road segment the part of a road between two subsequent intersections.
- 2.
Please note that we neglect walking distances in this consideration.
References
Shoup, D.C.: Cruising for parking. Transp. Policy 13(6), 479–486 (2006)
Lin, T., Rivano, H., Mouël, F.L.: A survey of smart parking solutions. IEEE Trans. Intell. Transp. Syst. PP(99), 1–25 (2017)
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)
Teodorović, D., Lučić, P.: Intelligent parking systems. Eur. J. Oper. Res. 175(3), 1666–1681 (2006)
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)
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)
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)
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). https://doi.org/10.1007/978-3-319-33783-8_17
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. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6569416. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6569416
SFMTA: SFpark: putting theory into practice. Pilot project summary and lessons learned (2014). http://sfpark.org/resources/docs_pilotsummary/. Accessed 24 June 2016
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)
Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49(11), 32–39 (2011)
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)
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)
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). http://doi.acm.org/10.1145/3003965.3003973
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)
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)
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)
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). http://linkinghub.elsevier.com/retrieve/pii/S0198971508000689
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). http://linkinghub.elsevier.com/retrieve/pii/S0965856414000470
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)
Acknowledgment
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.
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Bock, F., Di Martino, S., Sester, M. (2019). What Is the Impact of On-street Parking Information for Drivers?. In: Kawai, Y., Storandt, S., Sumiya, K. (eds) Web and Wireless Geographical Information Systems. W2GIS 2019. Lecture Notes in Computer Science(), vol 11474. Springer, Cham. https://doi.org/10.1007/978-3-030-17246-6_7
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