  The prediction of express delivery sequence, i.e., modeling and estimating
the volumes of daily incoming and outgoing parcels for delivery, is critical
for online business, logistics, and positive customer experience, and
specifically for resource allocation optimization and promotional activity
arrangement. A precise estimate of consumer delivery requests has to involve
sequential factors such as shopping behaviors, weather conditions, events,
business campaigns, and their couplings. Besides, conventional sequence
prediction assumes a stable sequence evolution, failing to address complex
nonlinear sequences and various feature effects in the above multi-source data.
Although deep networks and attention mechanisms demonstrate the potential of
complex sequence modeling, extant networks ignore the heterogeneous and
coupling situation between features and sequences, resulting in weak prediction
accuracy. To address these issues, we propose DeepExpress - a deep-learning
based express delivery sequence prediction model, which extends the classic
seq2seq framework to learning complex coupling between sequence and features.
DeepExpress leverages an express delivery seq2seq learning, a
carefully-designed heterogeneous feature representation, and a novel joint
training attention mechanism to adaptively map heterogeneous data, and capture
sequence-feature coupling for precise estimation. Experimental results on
real-world data demonstrate that the proposed method outperforms both shallow
and deep baseline models.
