Package Pick-up Route Prediction via Modeling Couriers’ Spatial-Temporal Behaviors

Abstract

Over 10 billion packages are picked up every day in China. Accurate prediction of couriers' pick-up routes can help the dispatch system to assign packages to couriers more intelligently, which is able to further increase the pick-up efficiency and reduce the overdue rate. In the package pick-up scene, the decision-making of a courier is quite complex since it's affected by strict spatial-temporal constraints (e.g., package location, promised pick-up time, current time and courier's current location). In this paper, we propose a novel model, named DeepRoute, to predict couriers' future package pick-up routes according to the couriers' decision experience learnt from their historical spatial-temporal behaviors. Specifically, DeepRoute consists of three layers: 1) The representation layer produces experience-aware representations for unpicked-up packages. 2) The transformer encoder layer encodes the representations of packages while considering the spatial-temporal correlations among them. 3) The attention-based decoder layer uses the attention mechanism to generate the whole pick-up route recurrently. Experiments on a real-world logistics dataset demonstrate the state-of-the-art performance of our DeepRoute model.