An empirical analysis of intention of use for bike-sharing system in China through machine learning techniques

Abstract

Sharing bicycles, as boosted by the advanced mobile technologies, is expected to mitigate the traffic congestion and air pollution issues in China. A survey study was conducted with 335 valid samples to identify the key factors that influence the customers' intention of use for bike-sharing system and quantify the corresponding importance. Five machine learning techniques for classification are applied and results are compared. The best performed technique is selected to prioritise and quantify the importance level of the influencing factors. The results indicate that the perceived ease of use is the most significant factor for the intention to use sharing bikes.

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