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.
Highlights
By reducing vehicle ownership, car-sharing can contribute to the conservation of resources and alleviation of traffic congestion [1]
On the basis of the optimal results obtained from the previous clustering experiments, the car-sharing users are classified into three groups according to their total expenditure amount
Despite having the smallest number of users, the first group greatly contributes to the revenue of car-sharing companies (68.9%). This group is defined as a high-revenue contribution group (HG)
Summary
Car-sharing can contribute to the conservation of resources and alleviation of traffic congestion [1]. Car-sharing companies face many challenges as the number of their users increases These companies face the “profit anxiety” problem, which results from their high investment and low revenue during the process of their rapid development. This problem is driven by their inaccurate identification of the revenue contribution level of their users. Losing high-level revenue contribution users will seriously damage the economic interests of a company. In this case, car-sharing companies blindly expand their scale of operations without considering the expenditure level of their users, thereby leading to higher costs and jeopardizing their development. The expenditure behavior of car-sharing users must be analyzed and a user management model must be established
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