# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 3 Methodology  
### Carsharing Simulation  
This paper used a simulation model for a station based one-way service, which allowed customers to use a car and return it to a different station. Reservation based carsharing was applied, i.e., all the customers were required to reserve a vehicle in advance. Round-trip and one-way services were considered, and if a company operated a one-way service, it also allowed customers to use the round-trip service since the round-trip service is a standard service for car sharing businesses. The proportion of round-trip and one-way reservations was assumed to be 50:50, i.e., one-way reservations comprised 50% of the total reservations with the remainder being round-trip reservations. The simulation was run for every reasonable combination of parameters (flexible parameters), such as the number of cars, number of reservations, and maximum booking time. Customer travel demand of the carsharing company (fixed parameters) was used as a simulation input, such as vehicle hour trips (VHT), time of day, day of the week, number of stations, and trip distribution of the stations. Forecasting and rebalancing relocation models were compared for each simulation scenario, as shown in Figure 1.  
First, simulation scenarios containing artificial data (reservation, station, and vehicle data) were generated by the trip generator, based on customer travel demand, the relocation model, number of vehicles, and reservations. Different combinations of parameters generated different simulation scenarios. Figure 1: Simulation architecture. Second, the simulator evaluated every scenario, i.e., the combination of travel demands with the number of cars, number of reservations, and the relocation model. For each scenario, the generated artificial data was updated once the simulation process was completed. The simulation checks customer reservations, so some reservation, station, and vehicle data changed during the simulation process. Finally, the simulation results were presented and analyzed to define the performance of the relocation model in one-way car sharing. Output parameters included the utilization ratio, acceptance ratio, and relocation cost. The simulation implemented DES, generating a list of events from the reservation based one-way system, such as calling, assign, starting, and ending times. A reservation via phone/internet (calling time) is required. Once the reservation is accepted, the system assigns a parked car to a particular reservation. Customers visit the departure station at the starting time to pick up the booked car and return it to the destination station at the ending time. In this study, last-minute reservation based one-way carsharing system with 10 stations will be evaluated by the simulation model. The reservation system allows customers to request a reservation with a maximum of three hours before the starting time. To guarantee the vehicle availability to the customer, we used a strict policy for reservation, i.e., the car is reserved until it is picked-up by the customer. Previous research ([PERSON] et al. [18]) used dynamic relocation (relocate during the day) and safety gap (i.e., 30 min of additional time, starting at the end time reservation to guarantee the space at the destination station; and avoid the car being reserved and relocated until the end of the safety gap duration) [18]. In our study, static relocation is used as we only focus on the performance of the forecasting relocation model at the end of the day. Different operational policies were applied in our study, such as when the customer made the reservation, a space at the destination station was reserved by the system starting from the reservation time until the end of the simulation time. It will prevent other customers from parking their car until the status of the particular space is changed to free again (i.e., the car is out from the station). In addition, we assumed that every customer returned the car at the returning time precisely, thus the next reservation is ready to be made.