crowddeliver: Planning City-Wide Package Delivery Paths Leveraging the Crowd of Taxis
crowddeliver: Planning City-Wide Package Delivery Paths Leveraging the Crowd of Taxis
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CROWDDELIVER：利用出租车人群规划全市包裹递送路径
DOI：
10.1109/tits.2016.2607458
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发表时间：
2017-06
影响因子：
8.5
通讯作者：
Sha Edwin
中科院分区：
文献类型：
--
作者：
Chen Chao;Zhang Daqing;Ma Xiaojuan;Guo Bin;Wang Leye;Wang Yasha;Sha Edwin
尽管人们对包裹快递服务有着巨大的需求和尝试，但在线零售商还没有一个切实可行的解决方案来让这种服务盈利。在本文中，我们提出了一种经济的快递包裹递送方法，即利用有乘客的出租车中继来帮助集体运输包裹，而不会降低乘客服务的质量。具体来说，我们提出了一个名为crowddeliver的两阶段框架，用于包裹交付路径规划。在第一阶段，我们离线挖掘历史出租车轨迹数据，以确定给定任何始发目的地对的估计行程时间最短的包裹递送路径。在第二阶段，以路径和行程时间为参考，开发了一种在线自适应出租车调度算法，根据实时请求迭代找到接近最优的交付路径，并相应地指导包裹路由。结果显示，超过85%的包裹可以在8小时内送达，平均约4.2次出租车接力。
Despite the great demand on and attempts at package express shipping services, online retailers have not yet had a practical solution to make such services profitable. In this paper, we propose an economical approach to express package delivery, i.e., exploiting relays of taxis with passengers to help transport package collectively, without degrading the quality of passenger services. Specifically, we propose a two-phase framework called crowddeliver for the package delivery path planning. In the first phase, we mine the historical taxi trajectory data offline to identify the shortest package delivery paths with estimated travel time given any Origin–Destination pairs. Using the paths and travel time as the reference, in the second phase we develop an online adaptive taxi scheduling algorithm to find the near-optimal delivery paths iteratively upon real-time requests and direct the package routing accordingly. Finally, we evaluate the two-phase framework using the real-world data sets, which consist of a point of interest, a road network, and the large-scale trajectory data, respectively, that are generated by 7614 taxis in a month in the city of Hangzhou, China. Results show that over 85% of packages can be delivered within 8 hours, with around 4.2 relays of taxis on average.
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DOI：
10.1109/tmc.2003.1255648
发表时间：
2003-10
期刊：
IEEE Trans. Mob. Comput.
影响因子：
--
作者：
M. Zorzi;R. Rao
通讯作者：
M. Zorzi;R. Rao
DOI：
10.1109/greencom-ithings-cpscom.2013.175
发表时间：
2013-08
期刊：
2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing
影响因子：
--
作者：
Guande Qi;Gang Pan;Shijian Li;Zhaohui Wu;Daqing Zhang;Lin Sun;L. Yang
通讯作者：
Guande Qi;Gang Pan;Shijian Li;Zhaohui Wu;Daqing Zhang;Lin Sun;L. Yang
DOI：
--
发表时间：
2015
期刊：
--
影响因子：
--
作者：
Bin Guo
通讯作者：
Bin Guo
DOI：
10.1007/978-3-642-31205-2_4
发表时间：
2012-06
期刊：
--
影响因子：
--
作者：
P. S. Castro;Daqing Zhang;Shijian Li
通讯作者：
P. S. Castro;Daqing Zhang;Shijian Li
DOI：
10.1109/tits.2014.2328231
发表时间：
2015-02-01
影响因子：
8.5
作者：
Zhang, Daqing;Sun, Lin;Wu, Zhaohui
通讯作者：
Wu, Zhaohui