Knowledge Commons of Institute of Automation，CAS
| Payment behavior prediction on shared parking lots with TR-GCN | |
| Xu, Qingyu1,2; Zhang, Feng1,2; Zhang, Mingde1,2; Zhai, Jidong3; He, Bingsheng4,5; Yang, Cheng6; Zhang, Shuhao7; Lin, Jiazao8,9; Liu, Haidi10; Du, Xiaoyong1,2 | |
| 发表期刊 | VLDB JOURNAL
 |
| ISSN | 1066-8888 |
| 2022-01-09 | |
| 页码 | 24 |
| 通讯作者 | Zhang, Feng(fengzhang@mc.edu.cn) |
| 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. |
| DOI | 10.1007/s00778-021-00722-0 |
| 关键词[WOS] | FRAMEWORK ; NETWORKS |
| 收录类别 | SCI |
| 语种 | 英语 |
| 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 |
| 项目资助者 | 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 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Hardware & Architecture ; Computer Science, Information Systems |
| WOS记录号 | WOS:000740399200001 |
| 出版者 | SPRINGER |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/47176 |
| 专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
| 通讯作者 | Zhang, Feng |
| 作者单位 | 1.Renmin Univ China, Key Lab Data Engn & Knowledge Engn MOE, Beijing, Peoples R China 2.Renmin Univ China, Sch Informat, Beijing, Peoples R China 3.Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China 4.Natl Univ Singapore, Sch Comp, Singapore, Singapore 5.NUS Ctr Trust Internet & Community, Singapore, Singapore 6.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China 7.Singapore Univ Technol & Design, Informat Syst Technol & Design Pillar, Singapore, Singapore 8.Peking Univ, Dept Informat Management, Beijing, Peoples R China 9.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 10.Zhongzhi Huaching Beijing Technol Co, 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. |
| 条目包含的文件 | 条目无相关文件。 | |||||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论