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Payment behavior prediction on shared parking lots with TR-GCN

文献类型：期刊论文

作者Xu, Qingyu2,8; Zhang, Feng2,8; Zhang, Mingde2,8; Zhai, Jidong9; He, Bingsheng3,4; Yang, Cheng5; Zhang, Shuhao1; Lin, Jiazao7,10; Liu, Haidi6; Du, Xiaoyong2,8
刊名VLDB JOURNAL
出版日期2022-01-09
页码24
ISSN号1066-8888
DOI10.1007/s00778-021-00722-0
通讯作者Zhang, Feng(fengzhang@mc.edu.cn)
英文摘要Shared parking lots are new types of sharing economy and generate a large social impact in our daily lives. Post-use payment is a hallmark method in the shared parking lots: it reflects trust in users and brings convenience to everyone. Accordingly, payment behavior prediction via data science technology becomes extremely important. We cooperate with a real intelligent parking platform, ThsParking, which is one of the top smart parking platforms in China, to study payment prediction, and encounter three challenges. First, we need to process a large volume of data generated every day. Second, a variety of parking related data shall be utilized to build the prediction model. Third, we need to consider the temporal characteristics of input data. In response, we propose TR-GCN, a temporal relational graph convolutional network for payment behavior prediction on shared parking lots, and we build a reminder to remind unpaid users. TR-GCN addresses the aforementioned challenges with three modules. 1) We develop an efficient data preprocessing module to extract key information from big data. 2) We build a GCN-based module with user association graphs from three different perspectives to describe the diverse hidden relations among data, including relations between user profile, temporal relations between parking patterns, and spatial relations between different parking lots. 3) We build an LSTM-based module to capture the temporal information from historical events. Experiments based on 50 real parking lots show that our TR-GCN achieves 91.2% accuracy, which is about 7% higher than the state-of-the-art and the reminder service makes more than half of the late-payment users pay, saving 1.9% loss for shared parking lots.
WOS关键词FRAMEWORK ; NETWORKS
资助项目National Key R&D Program of China[2020AAA0105200] ; National Natural Science Foundation of China[61732014] ; National Natural Science Foundation of China[62172419] ; National Natural Science Foundation of China[U20A20226] ; National Natural Science Foundation of China[61802412] ; Tsinghua University Initiative Scientific Research Program[20191080594] ; GHfund A[20210701] ; CCF-Tencent Open Research Fund ; NUS Centre for Trusted Internet and Community
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000740399200001
出版者SPRINGER
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Tsinghua University Initiative Scientific Research Program ; GHfund A ; CCF-Tencent Open Research Fund ; NUS Centre for Trusted Internet and Community
源URL[http://ir.ia.ac.cn/handle/173211/47175]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Zhang, Feng
作者单位1.Singapore Univ Technol & Design, Informat Syst Technol & Design Pillar, Singapore, Singapore
2.Renmin Univ China, Key Lab Data Engn & Knowledge Engn MOE, Beijing, Peoples R China
3.NUS Ctr Trust Internet & Community, Singapore, Singapore
4.Natl Univ Singapore, Sch Comp, Singapore, Singapore
5.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China
6.Zhongzhi Huaching Beijing Technol Co, Beijing, Peoples R China
7.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
8.Renmin Univ China, Sch Informat, Beijing, Peoples R China
9.Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China
10.Peking Univ, Dept Informat Management, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Qingyu,Zhang, Feng,Zhang, Mingde,et al. Payment behavior prediction on shared parking lots with TR-GCN[J]. VLDB JOURNAL,2022:24.
APA Xu, Qingyu.,Zhang, Feng.,Zhang, Mingde.,Zhai, Jidong.,He, Bingsheng.,...&Du, Xiaoyong.(2022).Payment behavior prediction on shared parking lots with TR-GCN.VLDB JOURNAL,24.
MLA Xu, Qingyu,et al."Payment behavior prediction on shared parking lots with TR-GCN".VLDB JOURNAL (2022):24.

入库方式： OAI收割

来源：自动化研究所

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