@inproceedings{76e521ae1d9d4404b56fd6a7c4907cee,
title = "Poster - 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 takeout 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.",
keywords = "Food delivery, Optimization, Spatial crowdsouring",
author = "Yan Liu and Bin Guo and He Du and Zhiwen Yu and Daqing Zhang and Chao Chen",
note = "Publisher Copyright: {\textcopyright} 2017 Copyright is held by the owner/author(s).; 23rd Annual International Conference on Mobile Computing and Networking, MobiCom 2017 ; Conference date: 16-08-2017 Through 20-08-2017",
year = "2017",
month = oct,
day = "4",
doi = "10.1145/3117811.3131268",
language = "English",
series = "Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM",
publisher = "Association for Computing Machinery",
pages = "564--566",
booktitle = "MobiCom 2017 - Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking",
}