Large-Scale Intelligent Taxicab Scheduling: A Distributed and Future-Aware Approach
Large-Scale Intelligent Taxicab Scheduling: A Distributed and Future-Aware Approach
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大规模智能出租车调度：分布式且面向未来的方法
DOI：
10.1109/tvt.2020.2997712
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发表时间：
2020-08
影响因子：
6.8
通讯作者：
Wenhua Li
中科院分区：
文献类型：
--
作者：
Yang Wang;Zhengyang Zhou;Kai Liu;Xike Xie;Wenhua Li
智能手机上的智能出租车调度系统继续受到欢迎，因为它们为城市出行提供了显著的便利，并为出租车司机增加了潜在的利润，从而为智能交通和城市商业注入了繁荣和活力。现有的调度方法往往以满足即时订单接受或最大化全局业务成功率为单一目标，存在偏差和短视，而多因素之间高度复杂的时空依赖关系和大规模订单流的效率瓶颈使得出租车调度问题仍然具有挑战性.为此，在我们的论文中，我们提出了一个集成的调度算法与未来感知和上下文感知机制。特别是，我们首先提出了一个广义的基于图的框架，其目的是捕捉交通依赖性，提供精确和量化的出租车调度的供需预测。然后，我们开发了区域供需背景的测量方法来执行协作和分布式调度。为解决大规模订单流场景下的效率瓶颈问题，本文设计了一个模型来学习业务模式，同时考虑司机和乘客的利益，并通过一种新的双激励策略进一步提高服务交付率。广泛的数值研究说明了显着的意义，我们的方法，在服务交付率和全球司机的收入。这些结果和新的视角使我们的算法成为一般时空调度任务的一个范例。
Intelligent taxicab scheduling systems on smartphones continue to gain popularity as they offer prominent conveniences for urban travelling as well as increase potential profits for taxicab drivers, hence inject prosperity and vitality into intelligent transportation and urban business. Existing scheduling approaches usually fall into biases and myopia due to their single target perspective of satisfying the immediate order acceptance, or maximizing the global business success rates.However, the highly complex spatiotemporal dependencies among multiple factors and the efficiency bottleneck of massive order flows make the taxicab scheduling issue still challenging. To this end, in our paper, we propose an integrated scheduling algorithm with both future-aware and context-aware mechanisms. In particular, we first present a generalized graph-based framework which aims to capture traffic dependencies, providing precise and quantified supply-demand prediction in taxicab scheduling. Then, we develop a measurement of regional supply-demand context to perform cooperative and distributed scheduling. To tackle the efficiency bottleneck in massive order flow scenario, we correspondingly design a model to learn business patterns with considering benefits from both drivers and passengers, and further promote service delivery rates via a novel bi-incentive strategy. Extensive numerical studies illustrate the remarkable significance of our method, in terms of both service delivery rates and global driver revenues. The promising results and brand-new perspectives enable our algorithm to be a paradigm in general spatiotemporal scheduling tasks.
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影响因子：
9.3
作者：
Ren, Ju;Guo, Hui;Zhang, Yaoxue
通讯作者：
Zhang, Yaoxue
在全球范围内高效地挖掘硬样本以进行人员重新识别
影响因子：
10.6
作者：
Hao Sheng;Yanwei Zheng;Wei Ke;Dongxiao Yu;Xiuzhen Cheng;Weifeng Lyu;Zhang Xiong
通讯作者：
Zhang Xiong
DOI：
10.1145/3357384.3357799
发表时间：
2019-10
期刊：
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
影响因子：
--
作者：
Ming Zhou;Jiarui Jin;Weinan Zhang;Zhiwei Qin;Yan Jiao;Chenxi Wang;Guobin Wu;Yong Yu;
通讯作者：
Ming Zhou;Jiarui Jin;Weinan Zhang;Zhiwei Qin;Yan Jiao;Chenxi Wang;Guobin Wu;Yong Yu;
DOI：
10.1145/2517351.2517361
发表时间：
2013-11
期刊：
--
影响因子：
--
作者：
Desheng Zhang;Ye Li;Fan Zhang;Mingming Lu;Yunhuai Liu;T. He
通讯作者：
Desheng Zhang;Ye Li;Fan Zhang;Mingming Lu;Yunhuai Liu;T. He
DOI：
10.1145/3219819.3220055
发表时间：
2018-07
期刊：
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
影响因子：
--
作者：
Nandani Garg;Sayan Ranu
通讯作者：
Nandani Garg;Sayan Ranu