%0 Conference Proceedings
%T Payment Behavior Prediction and Statistical Analysis for Shared Parking Lots
%+ School of Information [Renmin]
%+ Department of Computer Science and Technology (CST)
%+ Zhongzhi Huaching (Beijing) Technology Co., Ltd
%+ Ministry of Industry and Information Technology [Beijing] (MIIT)
%+ State Key Lab for Strength and Vibration (MOE)
%A Xu, Qingyu
%A Zhang, Feng
%A Zhang, Mingde
%A Zhai, Jidong
%A Lin, Jiazao
%A Liu, Haidi
%A Du, Xiaoyong
%Z Part 5: Big Data and Cloud
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 17th IFIP International Conference on Network and Parallel Computing (NPC)
%C Zhengzhou, China
%Y Xin He
%Y En Shao
%Y Guangming Tan
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-12639
%P 288-293
%8 2020-09-28
%D 2020
%R 10.1007/978-3-030-79478-1_25
%K Payment behavior
%K Payment time prediction
%K Shared parking lots
%Z Computer Science [cs]Conference papers
%X As the sharing economy is booming in China, many intelligent shared parking lots appear. Since more and more Chinese households own cars, it is necessary to study the payment behavior of shared parking lots, which may represent the entire sharing economy. In detail, we analyze the factors that influence users’ payment and predict users’ payment behavior of whether and when users will deliver parking bills after parking. We use 29,733 real parking records provided by Huaching Tech, a top smart parking company in China, in our study. After a comprehensive statistical analysis, we use decision tree model to predict users’ payment behavior. Experiments show that the decision tree model can reach 79% accuracy.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-03768744/document
%2 https://inria.hal.science/hal-03768744/file/511910_1_En_25_Chapter.pdf
%L hal-03768744
%U https://inria.hal.science/hal-03768744
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-12639