# Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
## 3 Methodology
### Data Collection  
This paper used a real carsharing service dataset from Korea, provided by HanCar (carsharing company in Korea), which operates in Seoul Metropolitan City. The customer reservation data was gathered for three months from May to July 2015. HanCar operates a reservation based round-trip service with 59 stations and 103 vehicles spread around the Seoul area. Figure 2 shows the carsharing station locations as red markers.  
Figure 2: Location of carsharing stations (red makers) separated by district area around Seoul.  
The simulation input follows the time of day travel pattern, where customer reservation proportions for morning, afternoon, and evening were 22.15%, 44.15%, and 33.70%, respectively, as shown in Figure 3a. The HanCar dataset showed that the average customer VHT was 120 min, with 30 min as the minimum and the maximum as one day (Figure 3b). However, in order to simplify our simulation input (but still follow the HanCar distribution), the trip duration is assigned for 2, 4, and 6 h and its distributions are 20%, 50%, and 30%, respectively. The percentage of trips was lowest at the beginning of the week, increasing as the week progressed, as shown in Figure 3c.  
The original 59 stations are grouped into 10 stations based on the district area (administrative unit). In Figure 2, the original stations and district boundaries are presented in red and blue colors respectively. In our dataset, the original stations mostly have three cars and their customer demands are almost similar. By grouping the original stations based on the district, it will create high unbalance demand since some of districts have more stations compared to others. It is expected that cars become unbalanced, with most cars ending up in unpopular departure groups, and most customers (from the high demand group) are unable to make a reservation. In this situation, the forecasting relocation can be evaluated in terms of its capability to solve the high unbalance demand problem. To simplify the simulation process, only the top 10 highest groups are used as inputs of the simulation. Based on the departure distribution of each group, customers mostly departed from Seongbuk-gu and Gyeongido-paju (38% and 22%, respectively), and the detailed departure distribution is shown in Figure 3d. However, the clustering based on the district is not the only solution, as other studies ([PERSON] et al. [18]) used different techniques to group the stations. The k-medoid based algorithm was used to cluster the stations.  
The HanCar company only offered round-trip service (i.e., the same destination and departure station), whereas the simulation also considered one-way service. [PERSON] et al. have proposed the  
Figure 3: Dataset distributions: (**a**) time of the day; (**b**) vehicle hour trips (VHT); (**c**) day of the week; and (**d**) customer departure.  
scenario to convert the round-trip data into one-way data [28]. Given the GPS data, the conversion is done by splitting the single round-trip data into multiple one-way data when the idle time of the rented vehicle at a given location exceeded one hour. However, due to the limitation of the dataset (i.e., GPS data), other types of conversion were applied. To generate the artificial reservation data for different departure and destination stations, the trip generator considered that the departure station followed the HanCar distribution and the destination station was assigned randomly.