Bike-Sharing Rebalancing Problems

Bruck, Bruno Petrato; Subramanian, Anand

doi:10.1007/978-3-030-54621-2_799-1

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…The BRP can be categorized into station-based systems and free-floating systems, depending on whether docking stations are used. Additionally, it can be divided into static BRP and dynamic BRP based on whether the user activity is negligible [3]. Building on the single commodity picking traveling salesman problem (1-PDTSP), the capacitated vehicle routing problem (CVRP), and the multiple traveling salesman problem (M-TSP), Dell'Amico et al [4] constructed a four-model mixed integer linear programming (MILP) model to formalize the BRP.…”

Section: Bike-sharing Rebalancing Problemmentioning

confidence: 99%

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

Ma,

Zheng,

Lin

et al. 2024

Preprint

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Public transportation scheduling aims to optimize the allocation of transportation resources, enhance transportation efficiency, and increase passenger satisfaction, which is crucial for building a sustainable urban transportation system. As a complement to public transportation, shared bikes provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, such as peak usage periods in the morning and evening and significant fluctuations in demand across different areas, the optimization of shared bike dispatch can better meet user needs, thereby reducing vehicle vacancy rates and increasing operating revenue. Addressing this issue, this paper designs a comprehensive decision-making approach for spatio-temporal demand prediction and bike dispatch optimization. For demand prediction, we designed a T-GCN based bike demand prediction model. In terms of dispatch optimization, we considered factors such as dispatch capacity, distance restrictions, and dispatch costs, and designed an optimization solution based on genetic algorithms. Finally, this paper validates the approach using shared bike operating data, shows that T-GCN can effectively predict short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper, which combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

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Section: Bike-sharing Rebalancing Problemmentioning

confidence: 99%

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

Ma,

Zheng,

Lin

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

Ma,

Zheng,

Lin

et al. 2024

Biomimetics

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Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

show abstract

Bike-Sharing Rebalancing Problems

Cited by 2 publications

References 45 publications

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

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