FooDNet: optimized on demand take-out food delivery using spatial crowdsourcing

Abstract : This paper builds a Food Delivery Network (FooDNet) that investigates the usage of urban taxis to support on demand take- out food delivery by leveraging spatial crowdsourcing. Unlike existing service sharing systems (e.g., ridesharing), the delivery of food in FooDNet is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge- number of delivery needs. In particular, we study the food delivery problem in association with the Opportunistic Online Takeout Ordering & Delivery service (O-OTOD). Specifically, the food is delivered incidentally by taxis when carrying passengers in the O-OTOD problem, and the optimization goal is to minimize the number of selected taxis to maintain a relative high incentive to the participated drivers. The two-stage method is proposed to solve the problem, consisting of the construction algorithm and the Large Neighborhood Search (LNS) algorithm. Preliminary experiments based on real-world taxi trajectory datasets verify that our proposed algorithms are effective and efficient
Document type :
Poster communications
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https://hal.archives-ouvertes.fr/hal-01643213
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Tuesday, November 21, 2017 - 11:14:43 AM
Last modification on : Sunday, October 20, 2019 - 9:48:01 AM

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Yan Liu, Bin Guo, He Du, Zhiwen Yu, Daqing Zhang, et al.. FooDNet: optimized on demand take-out food delivery using spatial crowdsourcing. MOBICOM 2017 : 23rd Annual International Conference on Mobile Computing and Networking, Oct 2017, Snowbird, United States. ACM, pp.564 - 566 2017, ⟨10.1145/3117811.3131268⟩. ⟨hal-01643213⟩

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