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| Main Authors: | , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2403.16029 |
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| _version_ | 1866909459428671488 |
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| author | Liu, Yining Ouyang, Yanfeng |
| author_facet | Liu, Yining Ouyang, Yanfeng |
| contents | Dockless electric micro-mobility services (e.g., shared e-scooters and e-bikes) have been increasingly popular in the recent decade, and a variety of charging technologies have emerged for these services. The use of charging stations, to/from which service vehicles are transported by the riders for charging, poses as a promising approach because it reduces the need for dedicated staff or contractors. However, unique challenges also arise, such as how to incentivize riders to drop off vehicles at stations and how to efficiently utilize the vehicles being charged at the stations. This paper focuses on dockless e-scooters as an example and develops a new spatial queuing network model to capture the steady-state scooter service cycles, battery consumption and charging processes, and the associated pricing and management mechanisms. Building upon this model, a system of closed-form equations is formulated and incorporated into a constrained nonlinear program to optimize the deployment of the service fleet, the design of charging stations (i.e., number, location, and capacity), user-based charging price promotions and priorities, and repositioning truck operations (i.e., headway and truck load). The proposed queuing network model is found to match very well with agent-based simulations. It is applied to a series of numerical experiments to draw insights into the optimal designs and the system performance. The numerical results reveal strong advantages of using charging stations for shared dockless electric micro-mobility services as compared to state-of-the-art alternatives. The proposed model can also be used to analyze other micromobility services and other charging approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16029 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Planning Charging Stations and Service Operations of Dockless Electric Micromobility Systems Liu, Yining Ouyang, Yanfeng Optimization and Control Dockless electric micro-mobility services (e.g., shared e-scooters and e-bikes) have been increasingly popular in the recent decade, and a variety of charging technologies have emerged for these services. The use of charging stations, to/from which service vehicles are transported by the riders for charging, poses as a promising approach because it reduces the need for dedicated staff or contractors. However, unique challenges also arise, such as how to incentivize riders to drop off vehicles at stations and how to efficiently utilize the vehicles being charged at the stations. This paper focuses on dockless e-scooters as an example and develops a new spatial queuing network model to capture the steady-state scooter service cycles, battery consumption and charging processes, and the associated pricing and management mechanisms. Building upon this model, a system of closed-form equations is formulated and incorporated into a constrained nonlinear program to optimize the deployment of the service fleet, the design of charging stations (i.e., number, location, and capacity), user-based charging price promotions and priorities, and repositioning truck operations (i.e., headway and truck load). The proposed queuing network model is found to match very well with agent-based simulations. It is applied to a series of numerical experiments to draw insights into the optimal designs and the system performance. The numerical results reveal strong advantages of using charging stations for shared dockless electric micro-mobility services as compared to state-of-the-art alternatives. The proposed model can also be used to analyze other micromobility services and other charging approaches. |
| title | Planning Charging Stations and Service Operations of Dockless Electric Micromobility Systems |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2403.16029 |