Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic Algorithm
Shared e-scooters are provided as a free-floating service that can be freely rented and returned within the service area. Although this has a positive effect in terms of convenience for users of shared e-scooters, it is creating new urban problems, such as undermining the aesthetics of the city and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/2696651 |
| _version_ | 1797847376398909440 |
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| author | Sujae Kim Gyeongjae Lee Sangho Choo |
| author_facet | Sujae Kim Gyeongjae Lee Sangho Choo |
| author_sort | Sujae Kim |
| collection | DOAJ |
| description | Shared e-scooters are provided as a free-floating service that can be freely rented and returned within the service area. Although this has a positive effect in terms of convenience for users of shared e-scooters, it is creating new urban problems, such as undermining the aesthetics of the city and obstructing the passage of pedestrians. Therefore, this study developed an optimal rebalancing algorithm to mitigate these problems and proposed an efficient operation plan. Complete relocation was performed to match the demand and supply for an efficient operation by reducing the unnecessary oversupply of shared e-scooters. The optimal rebalancing algorithm that reflects the attributes of e-scooters was developed through genetic algorithms and subsequently applied to actually used cases. The results indicate that when 20% of the potential demand was considered, an optimal solution could be derived with two relocation vehicles; however, when the potential demand was not considered, three relocation vehicles were required. Therefore, it is anticipated that the results of this study can serve as basic data for solving various urban problems caused by the recent rapid increase in the use of shared e-scooters. |
| first_indexed | 2024-04-09T18:10:25Z |
| format | Article |
| id | doaj.art-a8898b21005f4bea8001e00dd13b9ad5 |
| institution | Directory Open Access Journal |
| issn | 2042-3195 |
| language | English |
| last_indexed | 2024-04-09T18:10:25Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj.art-a8898b21005f4bea8001e00dd13b9ad52023-04-14T00:00:03ZengHindawi-WileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/2696651Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic AlgorithmSujae Kim0Gyeongjae Lee1Sangho Choo2Department of Urban PlanningDepartment of Urban PlanningDepartment of Urban Design and PlanningShared e-scooters are provided as a free-floating service that can be freely rented and returned within the service area. Although this has a positive effect in terms of convenience for users of shared e-scooters, it is creating new urban problems, such as undermining the aesthetics of the city and obstructing the passage of pedestrians. Therefore, this study developed an optimal rebalancing algorithm to mitigate these problems and proposed an efficient operation plan. Complete relocation was performed to match the demand and supply for an efficient operation by reducing the unnecessary oversupply of shared e-scooters. The optimal rebalancing algorithm that reflects the attributes of e-scooters was developed through genetic algorithms and subsequently applied to actually used cases. The results indicate that when 20% of the potential demand was considered, an optimal solution could be derived with two relocation vehicles; however, when the potential demand was not considered, three relocation vehicles were required. Therefore, it is anticipated that the results of this study can serve as basic data for solving various urban problems caused by the recent rapid increase in the use of shared e-scooters.http://dx.doi.org/10.1155/2023/2696651 |
| spellingShingle | Sujae Kim Gyeongjae Lee Sangho Choo Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic Algorithm Journal of Advanced Transportation |
| title | Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic Algorithm |
| title_full | Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic Algorithm |
| title_fullStr | Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic Algorithm |
| title_full_unstemmed | Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic Algorithm |
| title_short | Optimal Rebalancing Strategy for Shared e-Scooter Using Genetic Algorithm |
| title_sort | optimal rebalancing strategy for shared e scooter using genetic algorithm |
| url | http://dx.doi.org/10.1155/2023/2696651 |
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