How to effectively ensemble different base models is a challenging but extremely valuable task. This study focuses on the construction of an ensemble framework designed for spatio-temporal data to predict large-scale online taxi-hailing demand, where an attention-based deep ensemble net is designed to enhance the prediction accuracy. We present three attention blocks to model the inter-channel relationship, inter-spatial relationship and position relationship of the feature maps. Then, the attention maps can be multiplied by the input feature map for adaptive feature refinement. The proposed method is a kind of commonly used ensemble method which applies to large-scale spatio-temporal prediction. Experimental results on city-wide online taxi-hailing demand predictions demonstrate that our proposed attention-based ensemble net is superior to the existing ensemble strategy in terms of the prediction accuracy.


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    Title :

    Attention-Based Deep Ensemble Net for Large-Scale Online Taxi-Hailing Demand Prediction


    Contributors:
    Liu, Yang (author) / Liu, Zhiyuan (author) / Lyu, Cheng (author) / Ye, Jieping (author)


    Publication date :

    2020-11-01


    Size :

    1793861 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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