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Attention-Based Deep Ensemble Net for Large-Scale Online Taxi-Hailing Demand Prediction | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

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 f...Show More

Abstract:

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.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 21, Issue: 11, November 2020)
Page(s): 4798 - 4807
Date of Publication: 24 October 2019

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