Abstract
As parking accounting data of automatic payment system is accumulated, a managing parking fees in accordance with characteristics of parking utilization is expected. The purpose of this paper is to analyze the characteristics of parking utilization from a big data and to propose a procedure of parking fee management by developing of a simple simulator from a history of parking utilization. In concrete terms, we classify 1,050 parking lots by cluster analysis and analyze influence of a charge revision on parking time by survival analysis from 22.5 million parking accounting data in the past year. Further, we consider the appropriateness of modified fee by estimating parking time with a hazard-based duration model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Xu, R., Wunsch, D.C.: Clustering. Wiley (2009)
Hartigan, J.A., Wong, M.A.: A K-means Clustering Algorithm. Applied Statistics 28, 100–108 (1979)
Klein, J.P., Melvin, L.M.: Survival Analysis: Techniques for Censored and Truncated Data. Springer (2005)
Weibull, W.: A Statistical Distribution Function of Wide Applicability. Journal of Applied Mechanics 18, 293–297 (1951)
Kawaura, K.: The Study on Actual State of Parking in Service Area of Expressway (Part 2): Distribution of Parking Durations. Seisan Kenkyu 20(7), 362–364 (1968)
Iafrate, F.: A Journey from Big Data to Smart Data. Digital Enterprise Design & Management 261, 25–33 (2014)
Christopher, P.W., Harry, G., Robert, A.S.: Dynamic Revenue Management in Airline Alliances 44(1), 15–37 (2010)
Emmanuel, D.(M.)H., Suresh, K.N., Michael, P.: Production and Operations Management 19(6), 633–664 (2010)
Cenk, K., Kivilcim, D.: An empirical investigation of consumers’ willingness-to-pay and the demand function: The cumulative effect of individual differences in anchored willingness-to-pay responses 25(2), 139–152 (2014)
Hashimoto, S., Kanamori, R., Ito, T.: Auction-based Parking Reservation System with Electricity Trading. In: IEEE International Conference on Business Informatics (CBI), pp. 33–40 (2013)
Ishigaki, T., Takenaka, T., Motomura, Y.: Customer Behavior Prediction System by Large Scale Data Fusion in a Retail Service. The Japanese Society for Artificial Intelligence Journal 26(6), 670–681 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Enoki, Y., Kanamori, R., Ito, T. (2014). Managing Parking Fees Based on Massive Parking Accounting Data. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_91
Download citation
DOI: https://doi.org/10.1007/978-3-319-13560-1_91
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
eBook Packages: Computer ScienceComputer Science (R0)