# An Electric Fence-Based Intelligent Scheduling Method for Rebalancing Dockless Bike Sharing Systems
## 1 Introduction
They usually provide incentives or implement regulations to encourage users to participate in bike rebalancing. Existing user-based rebalancing methods can be divided into three types: best-of-two regulation, parking space reservation, and dynamic pricing incentives [27]. [PERSON] et al. presented a two-choice model in which every user is provided with two station choices at the time of a rental and is given an incentive if he/she chooses the station with the lower load [28]. They showed that even if a small portion of the users make the intended choices, the number of unbalanced stations is dramatically reduced. [PERSON] et al. investigated the parking space reservation problem by comparing the performance of complete, partial, and no parking space reservation policy. They found that the complete parking reservation policy achieved the lowest total excess time [29]. The dynamic pricing incentive is the most commonly used incentive to respond to the rapid changes in bike inventory levels. [PERSON] operated a V+ scheme to induce users to avoid certain stations and prefer others [30]. Fifteen minutes are added to their travel time if users place the bikes at one of the hundred uphill stations. Using a fluid approximation, [PERSON] et al. also developed a pricing strategy to solve the imbalance problem [31]. By rewarding users, a graph-theoretic approach was employed to solve the imbalance problem and maximize the profit of the system [26]. [PERSON] et al. determined the incentive price at each station dynamically to maximize the bike service level [32]. [PERSON] et al. and [PERSON] et al. considered the current and projected demand-supply condition in the incentive mechanism design [33; 34]. Specifically, [PERSON] et al. combined the vehicle-based strategy and user-based strategy by computing dynamically varying rewards for customers based on the current and predicted bike demand-supply conditions of the bike sharing systems [33]. [PERSON] et al. presented a crowdsourcing mechanism that employs the approach of regret minimization in online learning [34]. [PERSON] et al. also presented an incentive mechanism to encourage users to pick up/drop off bikes at neighboring stations to generate hub stations, which reduces the need for vehicle-based strategies [35]. A mixed-integer nonlinear and nonconvex problem was formulated by [PERSON] and [PERSON] to design the rebalance strategy under a static scenario. Compared with vehicle-based approaches, user-based approaches offer more flexible ways to rebalance the system [36; 37; 38]. According to the literature above, current vehicle-based methods on bike rebalancing problems mainly focus on the strategy design, either as a tactical problem (i.e., bike rebalancing under static scenario) or as an operational problem (i.e., bike rebalancing under dynamic scenario). However, vehicle-based methods operate on a routine basis; all demands cannot be satisfied until the next rebalancing operation. By incentivizing users to rebalance bikes on a real-time basis, the user-based rebalancing strategy effectively improves the service level of a bike sharing system. However, majority of existing user-based rebalancing studies employ a post-price model-based incentive mechanism, which increases the operation cost of the bike sharing systems. To solve the inappropriate parking problem, electric fences have been built as a choice for regulating inappropriate parking behaviors. An electric fence is a predetermined \"virtual fence\" without a physical installation. Users who park bikes outside the allowed areas cannot lock them and will continue to be charged [13; 39]. In this way, users will be guided by their application to proper parking locations. Electric fence policies and technology have been recommended in several important governmental documents, such as the \"National Guidance to Encourage and Regulate the Development of the Internet-based Dockless Bike-sharing Service\". Such technology and policies have also been tested as pilot projects in several cities in China since early 2017 [40].