# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 2 Literature Review
### Relocation Model  
Recent research in the area of carsharing has focused on the relocation model as an operational problem of imbalances in vehicle inventories across the network of stations. Simulation can provide a very detailed representation of carsharing operational services, and is typically used to identify system behavior when business constraints change. In this study, forecasting relocation was implemented for a last-minute reservation based one-way system. Multiple stations (station based or non-floating system) were used based on a real carsharing dataset. In reservation based carsharing system services, customers are usually able to make reservations by calling customer services, and/or using a web based reservation system. Once the reservation is accepted, the customer can arrive at the station and start using the car. After some time, the customer completes his/her travel and returns the car to the destination station. At the end of the day, the operator relocates a number of vehicles to increase the customer service level. In this scenario, certain events (calling, starting, ending, and relocation) that change system situations must be considered. In a discrete-event simulation, the operation of a system is represented as a chronological sequence of events. Each event occurs at an instance in time and marks a change of state in the system. Thus, in this case the discrete event simulation is needed to evaluate the one-way system given different events. [PERSON] and [PERSON] designed a simulation model to evaluate the overall system performance of a one-way system combined with operator based relocation [7]. Static relocation was proposed to move a car based on immediate needs at a particular station. They showed that predictive relocation based on historical data was more efficient in terms of reducing the customer wait time compared to static relocation. [PERSON] et al. proposed the shortest time and inventory balancing relocation methods. Shortest time relocation involves moving a car from a neighboring station in the shortest possible time, while inventory balancing relocation moves a car to a station with a shortage of cars from another station with an oversupply of cars [11]. They showed that inventory balancing reduced the relocation cost,but shortest time improved the zero vehicle time (ZVT), i.e., increased car utilization. [PERSON] et al. also tested different combinations of relocation models: static, shortest time, inventory balancing, and rebalancing (resetting the number of cars at each station at the end of the day, i.e., midnight) [27]. They showed that static shortest time generated higher profit and customer satisfaction compared to other relocation schemes. [PERSON] et al. have proposed an integrated optimization-simulation framework for one-way electric carsharing system [18]. The study considered station clustering in order to reduce the computation time for the optimization model. The clusters of stations are created using the technique similar to the k-Medoid algorithm. The simulation is used to test the feasibility of the optimization outcome in terms of the vehicle recharging requirement. The study considered the long-term reservation system which allows customers to perform reservations at least one day before (all demand is considered to be known in advance). The results showed that the long-term reservation can serve more demands from the customers as the operator can perform relocation to serve these reservations. On the other hand, [PERSON] et al. utilized a last-minute reservation system which allows customer to reserve a car for a trip starting in a short period of time [19]. If a demand is accepted, the assigned vehicle is kept reserved until it is picked up by the user who reserved the car. The study used an optimization framework to decide the best relocation plan in the coming hours (dynamic relocation). The relocation plan includes the information such as relocation choice, personnel tasking, and time for relocation; to serve the demands from customers.