# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 1 Introduction  
Increasing numbers of private vehicles provide high flexibility to users, but have several negative impacts, such as congestion, pollution, and excessive energy consumption. Public transport could be a good alternative, but has several limitations, such as the lack of service flexibility. Carsharing services can be a solution, providing the flexibility of private cars and the benefits of public transport. Previous research has shown the benefits of carsharing systems include cost savings, environmentally friendly transportation, and reducing gas consumption [1, 2]. Implementation of carsharing has reduced the CO\({}_{2}\) per average carsharing user in Germany [3], vehicle distance travelled in London [4], and vehicle distance travelled per year in North America [5].  
One-way carsharing systems enable a car to be driven between multiple stations, whereas for traditional services (round-trip) users must return the car to the originating station. Previous studies have shown that almost all round-trip operators viewed one-way carsharing as a complement to round-trip carsharing [6]. However, one-way carsharing systems present operational problems with unbalanced vehicle inventories across the station network [7]. Considerable effort has been undertaken to solve unbalanced car distribution, with several proposed approaches: user based relocation, i.e., allowing users to relocate the vehicles [8, 9, 10]; and operator based relocation, i.e., operators replenish vehicle stocks to high demand stations [7, 11]. Forecasting models for relocation have also been suggested to optimize relocation [12, 13].  
Studies have shown that to satisfy customer vehicle demand, forecasting relocation is required to predict the vehicle net flow for each station [12, 13]. [PERSON] and [PERSON] [7] showed that predictive relocation is more efficient in terms of reducing the customer wait time compared to other relocationsystems. Previous studies have mostly compared forecasting or predictive relocation performance with other relocation systems using forecasting model accuracy [12] and customer satisfaction [7; 13]. However, other evaluations are required, related to company profit, i.e., relocation cost and system utilization [11; 14]. Another critical factor that influences carsharing services is the different user behavior [15; 16; 17]. Thus, it is important that forecasting relocation performance evaluation includes the relocation costs, system utilization, and customer satisfaction, with respect to various types of customer transactions before being implemented as a practical business model.  
In addition, [PERSON] et al. [18] and [PERSON] et al. [19] have presented integrated optimization-simulation framework to evaluate the performance of long-term and last-minute one-way reservation systems. In long-term reservation, the one-way system can be optimized as all the demand is known in advance (reservations are performed at least a day before) thus it can serve a higher number of requests compared to last-minute reservation. However, the last-minute reservation gave more flexibility to the customer as it allows customers to reserve a car for a trip starting in a short period of time (a couple of hours). Our study proposed a simulation model to evaluate forecasting relocation for one way carsharing with last-minute reservation. The customer satisfaction (i.e., reservation acceptance ratio), relocation cost, and utilization ratio are used as performance indicators for a different number of customer demands. The simulation results provide a benchmark for management to optimize the relocation model for one-way services as well as to optimize the number of car operations based on their current customer trip demands.