# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 4 Result and Discussion
### Impact of the Number of Reservations and Cars on the Acceptance Ratio  
The simulation was run for 10 stations and 100 cars for a week. The input for comparison was the average number of reservations per day (x-axis), while the output was the acceptance ratio. Figure 12 shows that as the number of reservations increases, the acceptance ratio decreases (more customers do not receive a parked car). One-way schemes with relocation models showed better acceptance ratio compared to those without relocation. Forecasting relocation was superior in terms of the acceptance ratio compared to the rebalancing relocation and no relocation schemes. The forecasting model helps the operator predict car demand for each station, increasing the chance of customers renting a car. Rebalancing relocation also showed better performance than the no relocation scheme. Figure 13 shows the impact on the acceptance ratio for different numbers of cars. The simulation was run with an average of 1000 daily reservations and 10 stations for a week. The input for comparison was the total number of cars (x-axis), while the output was the acceptance ratio. All scenarios show that as the number of cars increases, the acceptance ratio increases (more customers rent a car). Forecasting relocation showed a better acceptance ratio compared to the other scenarios (rebalancing and no relocation). Forecasting relocation preferentially relocates cars to high demand stations, increasing the  
Figure 11: The impact of the number of cars on the utilization. Figure 10: Impact of the number of reservations on vehicle utilization. chance of a customer finding a parked car compared to rebalancing relocation. One-way services with rebalancing relocation were still superior to those without relocation. For the case of one-way service with 1000 daily reservations and forecasting relocation, the operator only requires 500 cars to satisfy almost all the customers (98.5% acceptance ratio). Increasing the number of customers (reservations) will decrease the acceptance ratio (more reservations are rejected by the system), whereas increasing the number of cars increases the acceptance ratio (more reservations are accepted by the system). Thus, the appropriate ratio of total number of customers (reservations per day) and total number of cars must be considered to maximize the acceptance ratio (customer satisfaction). If the operator allows one-way service, they must consider relocation models, since these increase the acceptance ratio compared to the no relocation scheme. Forecasting relocation provides the best option, due to allocating cars to high demand stations, leading to high customer satisfaction. Every company has a different assessment for the best relocation model for one-way service. Evaluation criteria such as relocation cost, acceptance ratio, and utilization ratio are not the only metrics to measure relocation model performance, but at least they provide a benchmark to understand the relocation model comparison. From the current study, companies can consider their policy in regards to two options. They can implement rebalancing relocation, reducing the relocation cost but also reducing utilization and customer satisfaction; or forecasting relocation, increasing relocation costs, but also increases utilization and customer satisfaction. However, other potential inputs should be considered as they can affect one-way carsharing service performance. [PERSON] et al. showed that station size also affects the utilization and acceptance ratio [PERSON] et al. (2015). [PERSON] discussed other potential inputs, such as the number of relocation staff, that also affect system performance [PERSON] et al. (2015). The current study set the station and staff size as unlimited,  
Figure 12: Impact of the number of reservations on the acceptance ratio. Figure 13: The impact of the number of cars on the acceptance ratio. and focused only on comparing relocation models. [PERSON] et al.