Abstract
Carsharing is regarded as a new mode of transportation that can meet the diversity of travel demands. Carsharing systems have different operating modes, and one-way systems are more widely used since cars can be dropped off at any station. However, their planning involves a series of joint decisions regarding the number, size, and location of stations, as well as the fleet size. This paper develops a data-driven mixed-integer linear programming (MILP) model for planning one-way carsharing systems that consider the spatial distribution of demand and the interacting decisions between stations. The characteristics of existing stations and their spatiotemporal correlations are an important part of the model. To solve the MILP model, the extension of the Benders decomposition algorithm is adopted. The practicality of the proposed approach is demonstrated in a case study in Beijing, China. The results show that the existing planning of carsharing could result in a serious waste of resources. In contrast, the proposed method can obtain effective results in a reasonable time. The location results corresponding to a different rate of satisfied demand show that increasing the parking spots to improve the interaction between stations can effectively reduce the cost of operations. It should be noted that this paper only considers the benefit of operators. Future works will be carried out to optimize the one-way carsharing system by considering the benefits of operators, as well as the benefits of users and society. In addition, the impact of COVID-19 will be taken into account in future modeling and case studies.
Highlights
Carsharing is a type of car rental service, which can be traced back to the late 1940s
Due to the nature of carsharing, many businesses are difficult to implement, such as vehicle relocation and personnel assignment. erefore, the commonly used modes are the first two, which are station-based. e one-way carsharing system is a station-based system that gives users more choice. This is the most popular carsharing system currently in most cities all over the world. erefore, how to optimize the one-way carsharing system has become an important issue that has attracted much attention. erefore, this paper focuses on the station location problem of a one-way carsharing system, determining the number, capacities, and locations of stations and the fleet size
An mixed-integer linear programming (MILP) model is established with the consideration of the coverage of the demand and the interaction of the car rental between stations. e major motivations and contributions of this paper are summarized as follows: (1) this paper analyzes the distribution and demand characteristics of an existing carsharing system; (2) this paper develops a data-driven optimization model, considering both the spatial coverage characteristics of the demand and the interaction of the car rental between stations; and (3) this paper attempts to solve the proposed model based on the extension of the Benders decomposition algorithm
Summary
Carsharing is a type of car rental service, which can be traced back to the late 1940s. E major motivations and contributions of this paper are summarized as follows: (1) this paper analyzes the distribution and demand characteristics of an existing carsharing system; (2) this paper develops a data-driven optimization model, considering both the spatial coverage characteristics of the demand and the interaction of the car rental between stations; and (3) this paper attempts to solve the proposed model based on the extension of the Benders decomposition algorithm. Jiao et al [18] proposed a mixed integer programming (MIP) model to meet the demand caused by limited electric vehicle power, taking into account charging mode and anxiety range They solved the location problem by assigning two types of charging piles to meet different types of demand. Considering the characteristics of AP clustering, the OD (origin-destination) of the taxi is divided based on the administrative area, and the results are optimized. e results show that the proposed method performs better than the standard K-means algorithm
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