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Identifying and Predicting the Expenditure Level Characteristics of Car-Sharing Users Based on the Empirical Data

Author

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  • Qiuyue Sai

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Jun Bi

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China)

  • Dongfan Xie

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Wei Guan

    (School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Car-sharing plays a positive role in reducing vehicle ownership and greenhouse gas emissions. However, the developmental contradictions between high investment and low revenues hinder the development of the car-sharing industry. Fully understanding car-sharing users can effectively ensure the healthy development of car-sharing companies and promote the development of the entire industry. To this end, this study attempts to develop a user management method that is based on user layering and prediction methods. By using order data from the Lan Zhou car-sharing company in China, this paper develops a clustering method for layering car-sharing users. A multi-layer perceptron model is also developed to categorize these users into different expenditure level categories while considering periodic features. Results show that new users can be divided into three categories according to their expenditures to car-sharing companies within 84 days. After 5 weeks of observation, the 84-day category of new users can be predicted with an accuracy of over 85%. These results provide scientific decision support for the user management and profitability of car-sharing companies.

Suggested Citation

  • Qiuyue Sai & Jun Bi & Dongfan Xie & Wei Guan, 2019. "Identifying and Predicting the Expenditure Level Characteristics of Car-Sharing Users Based on the Empirical Data," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6689-:d:291132
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    References listed on IDEAS

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    Cited by:

    1. Fei Ma & Dan Guo & Kum Fai Yuen & Qipeng Sun & Fuxia Ren & Xiaobo Xu & Chengyong Zhao, 2020. "The Influence of Continuous Improvement of Public Car-Sharing Platforms on Passenger Loyalty: A Mediation and Moderation Analysis," IJERPH, MDPI, vol. 17(8), pages 1-21, April.
    2. Xianbin Wang & Yuqi Zhao & Weifeng Li, 2023. "Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(3), pages 1-21, February.
    3. Shichao Sun & Yuanqian Liu & Yukun Yao & Zhengyu Duan & Xiaokun Wang, 2021. "The Determinants to Promote College Students’ Use of Car-Sharing: An Empirical Study at Dalian Maritime University, China," Sustainability, MDPI, vol. 13(12), pages 1-12, June.

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