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Online delivery route recommendation in spatial crowdsourcing

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Abstract

With the emergence of many crowdsourcing platforms, crowdsourcing has gained much attention. Spatial crowdsourcing is a rapidly developing extension of the traditional crowdsourcing, and its goal is to organize workers to perform spatial tasks. Route recommendation is an important concern in spatial crowdsourcing. In this paper, we define a novel problem called the Online Delivery Route Recommendation (OnlineDRR) problem, in which the income of a single worker is maximized under online scenarios. It is proved that no deterministic online algorithm for this problem has a constant competitive ratio. We propose an algorithm to balance three influence factors on a worker’s choice in terms of which task to undertake next. In order to overcome its drawbacks resulting from the dynamic nature of tasks, we devise an extended version which attaches gradually increased importance to the destination of the worker over time. Extensive experiments are conducted on both synthetic and real-world datasets and the results prove the algorithms proposed in this paper are effective and efficient.

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Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, China

    Dezhi Sun, Ke Xu, Hao Cheng, Tianshu Song, Rui Liu & Yi Xu

  2. China Academy of Telecommunications Technology and Datang Telecom Technology, Industry Group, Beijing, China

    Yuanyuan Zhang

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  1. Dezhi Sun
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  2. Ke Xu
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  3. Hao Cheng
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  4. Yuanyuan Zhang
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  5. Tianshu Song
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  6. Rui Liu
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  7. Yi Xu
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Correspondence to Rui Liu.

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This article belongs to the Topical Collection: Special Issue on Big Data Management and Intelligent Analytics

Guest Editors: Junping Du, Panos Kalnis, Wenling Li, and Shuo Shang

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Sun, D., Xu, K., Cheng, H. et al. Online delivery route recommendation in spatial crowdsourcing. World Wide Web 22, 2083–2104 (2019). https://doi.org/10.1007/s11280-018-0563-4

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