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Payment behavior prediction on shared parking lots with TR-GCN

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Abstract

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.

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Notes

  1. http://www.thsparking.com

  2. http://www.huaching.com

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Acknowledgements

This work is supported by the National Key R&D Program of China (2020AAA0105200), National Natural Science Foundation of China (No. 61732014, 62172419, U20A20226, and 61802412), Tsinghua University Initiative Scientific Research Program (20191080594), and GHfund A (No. 20210701). This work is also sponsored by CCF-Tencent Open Research Fund. Bingsheng’s work is in part supported by a research project grant under NUS Centre for Trusted Internet and Community. Feng Zhang is the corresponding author of this paper.

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Authors and Affiliations

  1. Key Laboratory of Data Engineering and Knowledge Engineering (MOE), and School of Information, Renmin University of China, Beijing, China

    Qingyu Xu, Feng Zhang, Mingde Zhang & Xiaoyong Du

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Jidong Zhai

  3. School of Computing, National University of Singapore, Singapore, Singapore

    Bingsheng He

  4. NUS Center for Trust Internet and Community, Singapore, Singapore

    Bingsheng He

  5. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

    Cheng Yang

  6. Information Systems Technology and Design pillar, Singapore University of Technology and Design, Singapore, Singapore

    Shuhao Zhang

  7. Department of Information Management, Peking University, Beijing, China

    Jiazao Lin

  8. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jiazao Lin

  9. Zhongzhi Huaching (Beijing) Technology Co., Beijing, China

    Haidi Liu

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  1. Qingyu Xu
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  2. Feng Zhang
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  10. Xiaoyong Du
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Xu, Q., Zhang, F., Zhang, M. et al. Payment behavior prediction on shared parking lots with TR-GCN. The VLDB Journal 31, 1035–1058 (2022). https://doi.org/10.1007/s00778-021-00722-0

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