Crowdsourcing Incentives for Multi-Hop Urban Parcel Delivery Network

Efficient and economic parcel delivery becomes a key factor in the success of online shopping. Addressing this challenge, this paper proposes to crowdsource the parcel delivery task to urban vehicles to utilize their spare capacities, thus improving the efficiency while reducing traffic congestions....

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Bibliographic Details
Published inIEEE access Vol. 7; pp. 26268 - 26277
Main Authors Hong, Huiting, Li, Xin, He, Daqing, Zhang, Yiwei, Wang, Mingzhong
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
auction-based mechanism
Biological system modeling
Cost analysis
Crowdsourcing
delivery tasks
Game theory
incentive model
Incentives
Logistics
Spread spectrum communication
Task analysis
Traffic congestion
Vehicles
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Summary:Efficient and economic parcel delivery becomes a key factor in the success of online shopping. Addressing this challenge, this paper proposes to crowdsource the parcel delivery task to urban vehicles to utilize their spare capacities, thus improving the efficiency while reducing traffic congestions. The delivery is planned as a multi-hop process, and participating vehicles will carry parcels from one shipping point to the next until they arrive at the destination, following the routes learned from the historical traffic statistics. The major contributions include an incentive framework to motivate the vehicles to participate in the delivery tasks by preserving the interests of the platform, the sender, and the crowd vehicles. Two incentive models are designed from platform-centric and user-centric perspectives, respectively. The platform-centric model first assesses an optimal reward <inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula> for parcel delivery with the principle of Stackelberg game, which enables the platform to maximize its profit. The user-centric model then applies a reverse auction mechanism to select the winning bids of vehicles while minimizing the sender cost, with truthfulness guarantee. Theoretical analysis and extensive experiments on a real urban vehicle trace dataset are provided to validate the efficacy of the proposed framework.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2896912