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The prediction of express delivery sequence, i.e., modeling and estimatingthe volumes of daily incoming and outgoing parcels for delivery, is criticalfor online business, logistics, and positive customer experience, andspecifically for resource allocation optimization and promotional activityarrangement. A precise estimate of consumer delivery requests has to involvesequential factors such as shopping behaviors, weather conditions, events,business campaigns, and their couplings. Besides, conventional sequenceprediction assumes a stable sequence evolution, failing to address complexnonlinear sequences and various feature effects in the above multi-source data.Although deep networks and attention mechanisms demonstrate the potential ofcomplex sequence modeling, extant networks ignore the heterogeneous andcoupling situation between features and sequences, resulting in weak predictionaccuracy. To address these issues, we propose DeepExpress - a deep-learningbased express delivery sequence prediction model, which extends the classicseq2seq framework to learning complex coupling between sequence and features.DeepExpress leverages an express delivery seq2seq learning, acarefully-designed heterogeneous feature representation, and a novel jointtraining attention mechanism to adaptively map heterogeneous data, and capturesequence-feature coupling for precise estimation. Experimental results onreal-world data demonstrate that the proposed method outperforms both shallowand deep baseline models.

DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction