United Parcel Service of America, Inc.

Donald Hickey

Ted Abebe

Ilya A Lavrik

Ed Hojecki

Vinay Rao

Elizabeth Barayuga

Jia Fan

I Lavrik

Embodiments are disclosed for determining delivery confidence intervals. An example method for determining a confidence interval includes the following operations. Delivery information is received from one or more sources, wherein the delivery information comprises data associated with at least one predefined location perimeter. The data associated with the at least one predefined location perimeter is normalized. The normalized data is categorized into training data used to perform a deep neural network regression analysis. A predicted delivery confidence interval is determined by constructing a predictive learning model by conducting a regression of the data using deep neural network regression. The predicted delivery confidence interval is stored in a results table in association with the predefined location perimeter. And, upon receiving a request from a visibility management system, accessing the results table to provide predicted delivery windows to consignees.

Systems and methods for providing delivery time estimates

Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

Automatically predicting shipper behavior using machine learning models

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Atlanta, GA, United States of America

Donald Hickey