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Statistical Analysis and Prediction of Parking Behavior

  • Ningxuan Feng
  • Feng ZhangEmail author
  • Jiazao Lin
  • Jidong Zhai
  • Xiaoyong Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11783)

Abstract

In China, more and more families own cars, and parking is also undergoing a revolution from manual to automatic charging. In the process of parking revolution, understanding parking behavior and making an effective prediction is important for parking companies and municipal policymakers.

We obtain real parking data from a big parking company for parking behavior analysis and prediction. The dataset comes from a shopping mall in Ningbo, Zhejiang, and it consists of 136,973 records in 396 days. Specifically, we mainly explore the impact of weather factors on parking behavior. We study several models, and find that the random forest model can make the most accurate parking behavior prediction. Experiments show that the random forest model can reach 89% accuracy.

Keywords

Prediction model Regression Weather condition 

Notes

Acknowledgments

This work is partially supported by the National Key R&D Program of China (Grant No. 2017YFB1003103), National Natural Science Foundation of China (Grant No. 61722208, 61732014, 61802412). Feng Zhang is the corresponding author (fengzhang@ruc.edu.cn).

References

  1. 1.
    Abdullatif, A., Masulli, F., Rovetta, S.: Tracking time evolving data streams for short-term traffic forecasting. Data Sci. Eng. 2(3), 210–223 (2017)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, C.C.: Outlier Analysis. Data Mining, pp. 237–263. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-14142-8_8CrossRefGoogle Scholar
  3. 3.
    Alho, A.R., Silva, J.D.A.E.: Freight-trip generation model: predicting urban freight weekly parking demand from retail establishment characteristics. Transp. Res. Rec. 2411(1), 45–54 (2014)CrossRefGoogle Scholar
  4. 4.
    Banti, K., Louta, M., Karetsos, G.: ParkCar: a smart roadside parking application exploiting the mobile crowdsensing paradigm. In: 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–6. IEEE (2017)Google Scholar
  5. 5.
    Caicedo, F., Blazquez, C., Miranda, P.: Prediction of parking space availability in real time. Expert Syst. Appl. 39(8), 7281–7290 (2012)CrossRefGoogle Scholar
  6. 6.
    Chen, X.: Parking occupancy prediction and pattern analysis. Dept. Comput. Sci., Stanford Univ., Stanford, CA, USA, Technical Report CS229-2014 (2014)Google Scholar
  7. 7.
    Fang, J., Ma, A., Fan, H., Cai, M., Song, S.: Research on smart parking guidance and parking recommendation algorithm. In: 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 209–212. IEEE (2017)Google Scholar
  8. 8.
    Florian, M., Los, M.: Impact of the supply of parking spaces on parking lot choice. Transp. Res. Part B: Methodol. 14(1–2), 155–163 (1980)CrossRefGoogle Scholar
  9. 9.
    Girden, E.R.: ANOVA: Repeated Measures, No. 84. Sage, Thousand Oaks (1992)CrossRefGoogle Scholar
  10. 10.
    Hans, C.: Bayesian lasso regression. Biometrika 96(4), 835–845 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hössinger, R., Widhalm, P., Ulm, M., Heimbuchner, K., Wolf, E., Apel, R., Uhlmann, T.: Development of a real-time model of the occupancy of short-term parking zones. Int. J. Intell. Transp. Syst. Res. 12(2), 37–47 (2014)Google Scholar
  12. 12.
    Kong, D., Li, F., Zhang, B.: Design and implementation of intelligent management system for urban road parking. In: Journal of Physics: Conference Series, vol. 1087, p. 062061. IOP Publishing (2018)Google Scholar
  13. 13.
    Lam, W.H., Tam, M., Yang, H., Wong, S.: Balance of demand and supply of parking spaces. In: 14th International Symposium on Transportation and Traffic Theory Transportation Research Institute (1999)Google Scholar
  14. 14.
    Le Cessie, S., Van Houwelingen, J.C.: Ridge estimators in logistic regression. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 41(1), 191–201 (1992)zbMATHGoogle Scholar
  15. 15.
    Liaw, A., Wiener, M., et al.: Classification and regression by randomForest. R. News 2(3), 18–22 (2002)Google Scholar
  16. 16.
    Liu, L., Yang, S., Peng, L., Li, X.: Hierarchical hybrid memory management in OS for tiered memory systems. IEEE Trans. Parallel Distrib. Syst. (2019)Google Scholar
  17. 17.
    Ottosson, D.B., Chen, C., Wang, T., Lin, H.: The sensitivity of on-street parking demand in response to price changes: a case study in Seattle, WA. Transp. Policy 25, 222–232 (2013)CrossRefGoogle Scholar
  18. 18.
    Pflügler, C., Köhn, T., Schreieck, M., Wiesche, M., Krcmar, H.: Predicting the availability of parking spaces with publicly available data. Informatik 2016 (2016)Google Scholar
  19. 19.
    Pierce, G., Shoup, D.: Getting the prices right: an evaluation of pricing parking by demand in San Francisco. J. Am. Plann. Assoc. 79(1), 67–81 (2013)CrossRefGoogle Scholar
  20. 20.
    Pierce, G., Shoup, D.: SFpark: pricing parking by demand (2013)Google Scholar
  21. 21.
    Quinn, J.: System and method for predicting parking spot availability, February 28 2008. US Patent App. 11/849,493Google Scholar
  22. 22.
    Rasmussen, C.E.: Gaussian processes in machine learning. In: Bousquet, O., von Luxburg, U., Rätsch, G. (eds.) ML-2003. LNCS (LNAI), vol. 3176, pp. 63–71. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-28650-9_4CrossRefGoogle Scholar
  23. 23.
    Roman, C., Liao, R., Ball, P., Ou, S., de Heaver, M.: Detecting on-street parking spaces in smart cities: performance evaluation of fixed and mobile sensing systems. IEEE Trans. Intell. Transp. Syst. 19(7), 2234–2245 (2018)CrossRefGoogle Scholar
  24. 24.
    Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Seabold, S., Perktold, J.: Statsmodels: econometric and statistical modeling with python. In: 9th Python in Science Conference (2010)Google Scholar
  26. 26.
    Seber, G.A., Lee, A.J.: Linear Regression Analysis, vol. 329. Wiley, Hoboken (2012)zbMATHGoogle Scholar
  27. 27.
    Shahzad, A., Choi, J.Y., Xiong, N., Kim, Y.G., Lee, M.: Centralized connectivity for multiwireless edge computing and cellular platform: a smart vehicle parking system. Wirel. Commun. Mob. Comput. 2018, 1–23 (2018)CrossRefGoogle Scholar
  28. 28.
    Shin, J.H., Kim, N., Jun, H.b., Kim, D.Y.: A dynamic information-based parking guidance for megacities considering both public and private parking. J. Adv. Transp. 2017, 1–19 (2017)CrossRefGoogle Scholar
  29. 29.
    Simhon, E., Liao, C., Starobinski, D.: Smart parking pricing: A machine learning approach. In: 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 641–646. IEEE (2017)Google Scholar
  30. 30.
    Tayade, Y., Patil, M.: Advance prediction of parking space availability and other facilities for car parks in smart cities. Int. Res. J. Eng. Technol. 3(5), 2225–2228 (2016)Google Scholar
  31. 31.
    Tilahun, S.L., Di Marzo Serugendo, G.: Cooperative multiagent system for parking availability prediction based on time varying dynamic markov chains. J. Adv. Transp. 2017, 1–14 (2017)CrossRefGoogle Scholar
  32. 32.
    Zhang, F., et al.: An adaptive breadth-first search algorithm on integrated architectures. J. Supercomput. 74(11), 6135–6155 (2018)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Ningxuan Feng
    • 1
  • Feng Zhang
    • 1
    Email author
  • Jiazao Lin
    • 2
  • Jidong Zhai
    • 3
  • Xiaoyong Du
    • 1
  1. 1.Key Laboratory of Data Engineering and Knowledge Engineering (MOE), and School of InformationRenmin University of ChinaBeijingChina
  2. 2.Department of Information ManagementPeking UniversityBeijingChina
  3. 3.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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