{"type":"ScholarlyArticle","title":"Dynamic Rebalancing Dockless Bike-Sharing System based on Station Community Discovery","authors":[{"name":"Jingjing Li","slug":"jingjing-li","given":"Jingjing","family":"Li","affiliation":""},{"name":"Qiang Wang","slug":"qiang-wang","given":"Qiang","family":"Wang","affiliation":""},{"name":"Wenqi Zhang","slug":"wenqi-zhang","given":"Wenqi","family":"Zhang","affiliation":""},{"name":"Donghai Shi","slug":"donghai-shi","given":"Donghai","family":"Shi","affiliation":""},{"name":"Zhiwei Qin","slug":"zhiwei-qin","given":"Zhiwei","family":"Qin","affiliation":""}],"abstract":"Influenced by the era of the sharing economy and mobile payment, Dockless Bike-Sharing System (Dockless BSS) is expanding in many major cities. The mobility of users constantly leads to supply and demand imbalance, which seriously affects the total profit and customer satisfaction. In this paper, we propose the Spatio-Temporal Mixed Integer Program (STMIP) with Flow-graphed Community Discovery (FCD) approach to rebalancing the system. Different from existing studies that ignore the route of trucks and adopt a centralized rebalancing, our approach considers the spatio-temporal information of trucks and discovers station communities for truck-based rebalancing. First, we propose the FCD algorithm to detect station communities. Significantly, rebalancing communities decomposes the centralized system into a distributed multi-communities system. Then, by considering the routing and velocity of trucks, we design the STMIP model with the objective of maximizing total profit, to find a repositioning policy for each station community. We design a simulator built on real-world data from DiDi Chuxing to test the algorithm performance. The extensive experimental results demonstrate that our approach outperforms in terms of service level, profit, and complexity compared with the state-of-the-art approach.","doi":"10.24963/ijcai.2021/569","url":"https://ijcai.org/proceedings/2021/569","pdf":"https://ijcai.org/proceedings/2021/0569.pdf","markdown":"https://ijcai.org/proceedings/2021/569.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/569","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Planning and Scheduling","track":"Main Track","number":569,"pages":{"first":4136,"last":4143},"keywords":["Planning and Scheduling: Applications of Planning","Planning and Scheduling: Planning and Scheduling"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}