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    SeqST-ResNet: A Sequential Spatial Temporal ResNet for Task Prediction in Spatial Crowdsourcing

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    Type
    Conference Paper
    Authors
    Zhai, Dongjun
    Liu, An
    Chen, Shicheng
    Li, Zhixu
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-04-24
    Online Publication Date
    2019-04-24
    Print Publication Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/652903
    
    Metadata
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    Abstract
    Task appearance prediction has great potential to improve task assignment in spatial crowdsourcing platforms. The main challenge of this prediction problem is to model the spatial dependency among neighboring regions and the temporal dependency at different time scales (e.g., hourly, daily, and weekly). A recent model ST-ResNet predicts traffic flow by capturing the spatial and temporal dependencies in historical data. However, the data fragments are concatenated as one tensor fed to the deep neural networks, rather than learning the temporal dependencies in a sequential manner. We propose a novel deep learning model, called SeqST-ResNet, which well captures the temporal dependencies of historical task appearance in sequences at several time scales. We validate the effectiveness of our model via experiments on a real-world dataset. The experimental results show that our SeqST-ResNet model significantly outperforms ST-ResNet when predicting tasks at hourly intervals and also during weekday and weekends, more importantly, in regions with intensive task requests.
    Citation
    Zhai D, Liu A, Chen S, Li Z, Zhang X (2019) SeqST-ResNet: A Sequential Spatial Temporal ResNet for Task Prediction in Spatial Crowdsourcing. Lecture Notes in Computer Science: 260–275. Available: http://dx.doi.org/10.1007/978-3-030-18576-3_16.
    Sponsors
    This research was supported in part by National Natural Science Foundation of China (NSFC) (Grant No. 61572336, 61632016, 61572335, 61772356), and Natural Science Research Project of Jiangsu Higher Education Institution (No. 18KJA520010, 17KJA520003), and the King Abdullah University of Science and Technology (KAUST), and the Blockshine Technology corp. Data sour Didi Chuxing.
    Publisher
    Springer Nature
    Journal
    Database Systems for Advanced Applications
    Conference/Event name
    24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
    DOI
    10.1007/978-3-030-18576-3_16
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-030-18576-3_16
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-18576-3_16
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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