DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction

TL;DR

DeepExpress is a novel deep learning model that accurately predicts express delivery volumes by modeling complex couplings between heterogeneous features and sequences, improving over existing methods.

Contribution

It introduces a seq2seq framework with heterogeneous feature representation and a joint training attention mechanism for better delivery sequence prediction.

Findings

Outperforms baseline models on real-world data

Effectively captures complex feature-sequence couplings

Improves prediction accuracy for express delivery volumes

Abstract

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.