Effective recycling planning for dockless sharing bikes

Abstract

Bike-sharing systems become more and more popular in the urban transportation system, because of their convenience in recent years. However, due to the high daily usage and lack of effective maintenance, the number of bikes in good condition decreases significantly, and vast piles of broken bikes appear in many big cities. As a result, it is more difficult for regular users to get a working bike, which causes problems both economically and environmentally. Therefore, building an effective broken bike prediction and recycling model becomes a crucial task to promote cycling behavior. In this paper, we propose a predictive model to detect the broken bikes and recommend an optimal recycling program based on the large scale real-world sharing bike data. We incorporate the realistic constraints to formulate our problem and introduce a flexible objective function to tune the trade-off between the broken probability and recycled numbers of the bikes. Finally, we provide extensive experimental results and case studies to demonstrate the effectiveness of our approach.