Hybrid rebalancing with dynamic hubbing for free-floating bike sharing systems
For managing the supply-demand imbalance in free-floating bike sharing systems, we propose dynamic hubbing (i.e., geofencing areas varying from one day to another) and hybrid rebalancing (combining user-based and operator-based strategies) and solve the problem with a novel multi-objective simulatio...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-09-01
|
| Series: | International Journal of Transportation Science and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2046043021000691 |
| _version_ | 1797761227399626752 |
|---|---|
| author | Vahid Mahmoodian Yu Zhang Hadi Charkhgard |
| author_facet | Vahid Mahmoodian Yu Zhang Hadi Charkhgard |
| author_sort | Vahid Mahmoodian |
| collection | DOAJ |
| description | For managing the supply-demand imbalance in free-floating bike sharing systems, we propose dynamic hubbing (i.e., geofencing areas varying from one day to another) and hybrid rebalancing (combining user-based and operator-based strategies) and solve the problem with a novel multi-objective simulation optimization approach. Given historical usage data and real-time bike GPS location information, the basic concept is that dynamic hubs are determined to encourage users to return bikes to desired areas towards the end of the day through a user incentive program. Then, for the remaining unbalanced bikes, an operator-based rebalancing operation will be scheduled. The proposed modeling and optimization solution algorithm determines the number of hubs, their locations, the start time for initiating the user incentive program, and the amount of incentive by considering two conflicting objectives, i.e., level of service and rebalancing cost. In this study, for free-floating bike sharing, the level of service is represented by the walking distance of users for locating a usable bike, which is different from level of service metrics commonly used by station-based bike sharing, and the rebalancing cost is weighted incentive credits plus operator-based rebalancing cost. We implemented the proposed method on the Share-A-Bull free-floating bike sharing system at the University of South Florida. Results show that a hybrid rebalancing and dynamic hubbing strategy can significantly reduce the total rebalancing cost and improve the level of service. Moreover, taking the advantage of crowd-sourcing (or job-sharing) reduces negative impacts—energy consumption and greenhouse gas emissions—of the operation of rebalancing vehicles and makes bike sharing a more promising environmentally friendly sharing transportation mode. |
| first_indexed | 2024-03-12T19:09:56Z |
| format | Article |
| id | doaj.art-3101856c4d6948f691eeca4f38d5ba39 |
| institution | Directory Open Access Journal |
| issn | 2046-0430 |
| language | English |
| last_indexed | 2024-03-12T19:09:56Z |
| publishDate | 2022-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Transportation Science and Technology |
| spelling | doaj.art-3101856c4d6948f691eeca4f38d5ba392023-08-02T05:58:05ZengKeAi Communications Co., Ltd.International Journal of Transportation Science and Technology2046-04302022-09-01113636652Hybrid rebalancing with dynamic hubbing for free-floating bike sharing systemsVahid Mahmoodian0Yu Zhang1Hadi Charkhgard2Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL 33620, USADepartment of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA; Corresponding author.Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL 33620, USAFor managing the supply-demand imbalance in free-floating bike sharing systems, we propose dynamic hubbing (i.e., geofencing areas varying from one day to another) and hybrid rebalancing (combining user-based and operator-based strategies) and solve the problem with a novel multi-objective simulation optimization approach. Given historical usage data and real-time bike GPS location information, the basic concept is that dynamic hubs are determined to encourage users to return bikes to desired areas towards the end of the day through a user incentive program. Then, for the remaining unbalanced bikes, an operator-based rebalancing operation will be scheduled. The proposed modeling and optimization solution algorithm determines the number of hubs, their locations, the start time for initiating the user incentive program, and the amount of incentive by considering two conflicting objectives, i.e., level of service and rebalancing cost. In this study, for free-floating bike sharing, the level of service is represented by the walking distance of users for locating a usable bike, which is different from level of service metrics commonly used by station-based bike sharing, and the rebalancing cost is weighted incentive credits plus operator-based rebalancing cost. We implemented the proposed method on the Share-A-Bull free-floating bike sharing system at the University of South Florida. Results show that a hybrid rebalancing and dynamic hubbing strategy can significantly reduce the total rebalancing cost and improve the level of service. Moreover, taking the advantage of crowd-sourcing (or job-sharing) reduces negative impacts—energy consumption and greenhouse gas emissions—of the operation of rebalancing vehicles and makes bike sharing a more promising environmentally friendly sharing transportation mode.http://www.sciencedirect.com/science/article/pii/S2046043021000691Hub-based rebalancingUser incentive programFree-floating bike sharingSimulationMulti-objective optimization |
| spellingShingle | Vahid Mahmoodian Yu Zhang Hadi Charkhgard Hybrid rebalancing with dynamic hubbing for free-floating bike sharing systems International Journal of Transportation Science and Technology Hub-based rebalancing User incentive program Free-floating bike sharing Simulation Multi-objective optimization |
| title | Hybrid rebalancing with dynamic hubbing for free-floating bike sharing systems |
| title_full | Hybrid rebalancing with dynamic hubbing for free-floating bike sharing systems |
| title_fullStr | Hybrid rebalancing with dynamic hubbing for free-floating bike sharing systems |
| title_full_unstemmed | Hybrid rebalancing with dynamic hubbing for free-floating bike sharing systems |
| title_short | Hybrid rebalancing with dynamic hubbing for free-floating bike sharing systems |
| title_sort | hybrid rebalancing with dynamic hubbing for free floating bike sharing systems |
| topic | Hub-based rebalancing User incentive program Free-floating bike sharing Simulation Multi-objective optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2046043021000691 |
| work_keys_str_mv | AT vahidmahmoodian hybridrebalancingwithdynamichubbingforfreefloatingbikesharingsystems AT yuzhang hybridrebalancingwithdynamichubbingforfreefloatingbikesharingsystems AT hadicharkhgard hybridrebalancingwithdynamichubbingforfreefloatingbikesharingsystems |