Location optimization of bicycle-sharing stations using multiple-criteria decision making

Abstract

A bicycle-sharing system not only allows citizens to freely use bicycles installed in specific locations but is also a supplement to public transportation. In this study, we aim to improve the accessibility to public bicycles by finding the optimal locations of bicycle-sharing stations based on the history and spatial data of public bicycle operations. IoT sensors record the bicycles’ movement information. Multiple linear regression analysis is used to select the most important criteria for a bicycle-sharing system. The selected criteria are then applied to multiple-criteria decision making (MCDM) to rank the potential locations of bicycle-sharing stations. The top-ranking locations are finally determined through an optimization stage.