# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 4 Result and Discussion
### Impact of the Number of Reservations and Cars on Relocation Cost  
Figure 8 shows the level of relocations required for the forecasting and rebalancing relocation models. The simulation was run for 10 stations and 100 cars for a week. The input was the average number of reservations per day (x-axis) and the output was the relocation cost (number of relocated cars). The choice of the relocation model was significant for the vehicle used (number of vehicles rented by customers). However, increasing the vehicles used for one-way carsharing leads to highly imbalanced distributions of cars at each station, and hence a high number of relocations. High relocation leads to negative impacts, due to the additional relocation cost. Operators must pay staff to move or drive cars from oversupplied to undersupplied car stations. This paper considered a daily relocation period (midnight) for both forecasting and rebalancing models to reduce the relocation cost. A 1 h relocation time was assumed at midnight, when cars would generally not be operated due to sparse reservations. The status of a relocated car should be a parked car, i.e., there was no existing reservation for the car. Forecasting and rebalancing relocation schemes were compared with non-relocation for one-way service. If the operator does not apply any relocation scheme, they need not consider relocation cost at the end of the day, i.e., the relocation cost was always 0. Figure 8 shows that the forecasting relocation model has a high relocation cost compared to rebalancing. Forecasting relocation sets the number of cars at each station differently based on the previous reservation history. If management operates forecasting relocation for a high number of reservations, most cars end up in unpopular departure stations (low demand stations). Thus, the operator needs to relocate a high number of cars from low to high demand stations at the end of the day (midnight). On the other hand, rebalancing relocation only resets the number of cars at each station to the same number of cars at the end of the day (midnight). Thus, the relocation cost is lower. With an increasing number of reservations, the relocation cost for forecasting relocation will increase and then become constant after \(\sim\)400 reservations per day, whereas the cost of rebalancing relocation becomes constant after \(\sim\)300 reservations per day. The scenario included 100 car operations. Therefore, when the number of reservations exceeds 400 per day, some reservations will be rejected (there is not enough free cars), and there will be no increase in vehicles used, i.e., no increase in relocated cars. Figure 9 shows relocation costs for different numbers of cars. The simulation assumed an average of 1000 daily reservations and 10 stations, and was run for a week. The input was the total number of operation cars (x-axis), and the output was the relocation cost (y-axis). Figure 8: Effect of the number of reservations on relocation cost. The relocation cost for rebalancing was similar to forecasting relocation. As the number of cars increased, vehicles used (cars rented by customers) also increased, thus the relocation costs for rebalancing and forecasting relocation also increased. High vehicle usage in one-way carsharing generates highly imbalanced car distributions at each station. For the case of one-way carsharing with forecasting relocation, when the number of cars exceeds 500, relocated cars remain constant, as all of the reservations will always be accepted, and thus there are no more relocation activities. Thus, if an operator allows one-way services, they must consider a relocation model to address imbalanced distribution of cars in each station. As the number of reservations and number of cars increase, relocation costs will also increase (Figures 8 and 9). Rebalancing relocation provides benefits by reducing relocation cost, and hence, increases profit.