United Parcel Service of America, Inc.

Ed Hojecki

Sumeet Pradeep Shroff

Ted Abebe

Ilya A Lavrik

Donald Hickey

Issac Azar

Rosalie Piccione

Brian Gerhard

Vinay Rao

I Lavrik

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

Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for determining delivery or pick-up windows. In one embodiment a method is provided comprising determining whether sufficient historical information/data to determine an estimated pick-up/delivery time is received for each weekday when deliveries are made and in response to determining that the sufficient historical information/data is available for a first weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the first weekday based on the sufficient historical information/data for the first serviceable point and for the first weekday. Similarly, in response to determining that the sufficient historical information/data is not available for a second weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the second weekday based on the first historical information/data.

Determining estimated pick-up/delivery windows using clustering

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Atlanta, GA, United States of America

Ed Hojecki