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• W4362636617 abstract "Package delivery via the Unmanned Aerial Vehicles (UAVs) is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the UAV’s limited energy storage makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework based on crowdsourcing, in which the UAVs can call on ground vehicles to provide hitch services for energy saving and extending their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for crowdsourced ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over the traffic network is studied on the basis of the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only." @default.
• W4362636617 created "2023-04-07" @default.
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• W4362636617 date "2023-08-01" @default.
• W4362636617 modified "2023-10-07" @default.
• W4362636617 title "Incentive mechanism and path planning for Unmanned Aerial Vehicle (UAV) hitching over traffic networks" @default.
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• W4362636617 doi "https://doi.org/10.1016/j.future.2023.03.048" @default.
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