Reposition optimization in the free-floating bike-sharing system considering transferring travels from urban rail transit. (April 2023)

• Record Type: Journal Article
• Authors: Pan, Xiaoyi; Tang, Jinjun; Yu, Tianjian; Cai, Jianming; Xiong, Yong; Gao, Fan
• Is Part Of: Computers & industrial engineering. Volume 178(2023)
• Publication Date: 2023-04
• Subjects: Reposition optimization -- Urban rail transit -- Free-floating bike-sharing -- Transfer -- Hybrid cuckoo search; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2023.109127
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A repositioning framework considering URT-FFBS transferring travel is proposed. A repositioning quantity determination method based on GWR model is applied. An optimization model that considers the carbon price and broken bikes is built. The HCS algorithm is designed to solve the repositioning problem in Shenzhen. The methods can balance the demand and supply of FFBS and promote URT ridership. Abstract: The popular free-floating bike-sharing (FFBS) system serves as a first- and last-mile solution to connect urban rail transit (URT). Recently, a number of studies have investigated how to enhance the integration between FFBS and URT. Nevertheless, how to reposition the FFBS system to promote URT ridership is still a challenge. This study proposes a bike-sharing repositioning framework to balance the demand and supply of FFBS and promote URT ridership. First, a repositioning quantity determination method based on the geographically weighted regression model is applied to determine the number of bikes to be delivered to or collected from each transfer area. Second, we establish an optimization model to minimize the total repositioning cost considering carbon price. Both the repositioning of normal bikes and recycling of broken bikes are included in this model. Subsequently, a hybrid Cuckoo Search algorithm is designed to solve the problem. The extended 2-opt, random removal and insertion algorithms are introduced to generate new solutions, which enhance the search ability. Finally, several numerical studies are conducted using data collected in Shenzhen, China, to verify the effectiveness of the proposed methods. The results demonstrate that the methodology proposed in this study is reasonable and effective. The findings are expected to guide traffic authorities to get environmental and highly utilized scheduling schemes for the integration between FFBS and URT systems.