Dynamic Rebalancing Dockless Bike-Sharing System based on Station Community Discovery

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.

Dynamic Workload-Aware Bike Rebalancing for Bike-Sharing Systems

“…Usually, static repositioning employs a smaller number of heavy vehicles (e.g., trucks) with larger capacity and greater travel range [21]. However, the effect of static repositioning is limited and can fail to match supply and demand quickly as the bikes start to move when the BSS operates during the day [14].…”

“…On the contrary, dynamic repositioning schedules for bike repositioning happen throughout the day when the BSS is in operation and considers the bike flows at different times of the day. Dynamic repositioning using trucks has been studied [20,6,14]. The use of a large number of small vehicles has also been studied a lot due to environmental concerns regarding heavy vehicles.…”

“…In practical bike rebalancing operations, when the coverage of bike stations is broad, and the user demands are high, we need to decompose the large BSS into smaller sub-networks for real-time operations [10,14,24]. We consider a set C = {1, 2, .…”

“…Then we extend the generic model of dynamic rebalancing and routing problem (DRRP) introduced by Ghosh et al [10], which is common in the literature (e.g., [17,7,14]), for defining our problem. For each sub-network, the problem can be represented using the following tuple: gives the initial distribution of bikes across stations belonging to sub-network c 1 .…”