The imbalance in bike-sharing systems between supply and demand is significant. Therefore, these systems need to relocate bikes to meet customer needs. The objective of this research is to increase the efficiency of bike-sharing systems regarding rebalancing problems. The prediction of the demand for bike sharing can enhance the efficiency of a bike-sharing system for the operation process of rebalancing in terms of the information used in planning by proposing an evaluation of algorithms for forecasting the demand for bikes in a bike-sharing network. The historical, weather and holiday data from three distinct databases are used in the dataset and three fundamental prediction models are adopted and compared. In addition, statistical approaches are included for selecting variables that improve the accuracy of the model. This work proposes the accuracy of different models of artificial intelligence techniques to predict the demand for bike sharing. The results of this research will assist the operators of bike-sharing companies in determining data concerning the demand for bike sharing to plan for the future. Thus, these data can contribute to creating appropriate plans for managing the rebalancing process.

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The number of bike-sharing services has rapidly increased in many cities worldwide. One of the main challenges of the bike-sharing system operation costs is allocating enough bikes and parking space. This paper presents a model for solving the bike-sharing relocation problem. The artificial bee colony (ABC) algorithm is an efficient approach, but it is still insufficient for the selection strategy. ABC has been adopted in various problems to improve the performance of various systems. This research proposed a modified ABC algorithm in a neighbor solution to enhance the solution performance, namely guided local search (GLS), to apply to the design route for transportation while truck relocation bikes each station in the bike-sharing system. Computational experiments were performed to find out the best modeling solution in the case. The implementations were experimental for the same data instances, which made it possible to compare the performance algorithms so as to solve the bike-sharing relocation problem of the pickup and drop off. The results showed that the GLS-ABC method can be a better solution than the original one. The statistically significant p-value of the mean objective value of the different algorithms was smaller than 0.05. Thus, the impact of minimizing the route tour cost in solving the bike-sharing relocation problem.