Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing

Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing

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A carsharing service can be seen as a transport alternative between private and public transport that enables a group of people to share vehicles based at certain stations.The advanced carsharing service, one-way carsharing, enables customers to return the car to another station.However, one-way here implementation generates an imbalanced distribution of cars in each station.

Thus, this paper proposes forecasting relocation to solve car distribution imbalances for one-way carsharing services.A discrete event simulation model was developed to help evaluate the proposed model performance.A real case dataset was used to find the best simulation result.

The results provide a clear insight into the impact of tenga flip orb forecasting relocation on high system utilization and the reservation acceptance ratio compared to traditional relocation methods.