Discovering Actual Delivery Locations from Mis-Annotated Couriers' Trajectories

Delivery locations are fundamental data source for intelligent logistics, which can be used in route planning, arrival time estimation, parcel allocation, etc. Using the Geocoded waybill location of an address as the delivery location is not sufficient, due to wrong address parsing, coarse-grained POI database, or different preferences of customers. To mitigate the insufficiency of Geocoding, some methods have been proposed, which utilize couriers' locations when waybills are confirmed to be delivered for delivery location inference. Nevertheless, these methods highly rely on the quality of couriers' annotations and fail when couriers confirm deliveries with delays. We propose to infer actual delivery locations of addresses from couriers' trajectories. This idea lies on an observation that the semantics of delivering a parcel are well captured by couriers' trajectories (e.g., a stay point would be generated when a delivery occurs), which holds even couriers confirm deliveries with delays. Specifically, we design Delivery Location Inference under Mis-Annotation (DLInfMA), which (1) generates location candidates from stay points in couriers' trajectories; (2) extracts features from both an address and its location candidates; and (3) uses an attention-based neural network model LocMatcher to predict the delivery location for each address. Experiments on two real-world datasets from JD Logistics as well as synthetic datasets demonstrate the effectiveness, robustness and scalability of DLInfMA. We also present a deployed system along with two applications based on DLInfMA.