Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours

Abstract

In recent years, free-floating bike-sharing systems (FFBSSs) have been considerably developed in China. As there is no requirement to construct bike stations, this system can substantially reduce the cost when compared to the traditional bike-sharing systems. However, FFBSSs have also become a critical cause of parking disorder, especially during the morning and evening rush hours. To address this issue, the local governments stipulated that FFBSSs are required to deploy virtual stations near public transit stations and major establishments. Therefore, the location assignment of virtual stations is sufficiently considered in the FFBSSs, which is required to solve the parking disorder and satisfy the user demand, simultaneously. The purpose of this study is to optimize the location assignment of virtual stations that can meet the growing demand of users by analyzing the usage data of their shared bikes. This optimization problem is generally formulated as a mixed-integer linear programming (MILP) model to maximize the user demand. As an alternative solution, this article proposes a clustering algorithm, which can solve this problem in real time. The experimental results demonstrate that the MILP model and the proposed method are superior to the K-means method. Our method not only provides a solution for maximizing the user demand but also gives an optimized design scheme of the FFBSSs that represents the characteristics of virtual stations.

A Systematic Review of Station Location Techniques for Bicycle-Sharing Systems Planning and Operation

“…The optimization of virtual station locations was addressed in a study of the DBSS of Beijing, China [31]. Although some studies aimed to find virtual stations or zones for new stations, some studies sought to pinpoint exact locations of new stations.…”

Characterizing the stocks, flows, and carbon impact of dockless sharing bikes in China

“…Since the spatial and temporal imbalance between demand (Gervini and Khanal, 2019;Zhou et al, 2018) and (re)distribution (Ho and Szeto, 2017;Li et al, 2016) of sharing bikes is identified as the key to successful SBP development, some researchers have used different repositioning technologies and models to optimize the station position and address congestion or starvation issues of IT-based SBP (Forma et al, 2015;Ghosh et al, 2017;Szeto and Shui, 2018). This is of particular importance for DSBs due to their flexibility without docking stations, so demand forecasting (Xu et al, 2018), static (Liu et al, 2018) and dynamic repositioning problems (Shui and Szeto, 2018), optimizing location (Sun et al, 2019) and optimizing transportation planning (Sayyadi and Awasthi, 2018) are the key focuses of DSBs research as well in the transportation literature.…”

Rebalancing stochastic demands for bike-sharing networks with multi-scenario characteristics

“…These studies may be categorised into three hierarchical levels, including strategic design, tactical development and operations management [9]. In terms of strategic design, research has been aimed to ensure satisfactory levels of service and inventory in bike-sharing networks through rational planning of the location and capacity of the stations and/or parking places, thereby reducing user dissatisfaction, station construction cost, or both, e.g., [10,11,12,13,14,15]. The studies at the tactical level have focused on formulating various user incentive policies in an effort to encourage users to ride shared bikes from oversupplied areas to undersupplied areas, e.g., [16,17,18].…”