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)


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.


Prediction model Regression Weather condition 



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 (


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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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