| CPC G06Q 10/0833 (2013.01) [G06Q 50/40 (2024.01)] | 22 Claims |
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1. A method of predicting an estimated time of arrival for an equipment via a railroad, the method including:
inputting to a trained machine learning model a shipment data that includes a starting location for a particular trip and a destination location for the particular trip, and at least one event data generated in response to an event during the particular trip of the equipment via the railroad from the starting location for the particular trip to the destination location for the particular trip,
wherein the event is a location update event that indicates movement of the equipment in proximity to a particular geographic location along the particular trip, and
wherein the particular geographic location is any location between the starting location and the destination location and the particular geographic location is identified by corresponding geographical coordinates
predicting, using the trained machine learning model, the estimated time of arrival of the equipment at the destination location for the particular trip when no historical trip data exists for the particular trip; and
wherein the trained machine learning model uses an ensemble of tree models for the predicting of the estimated time of arrival of the equipment at the destination location,
wherein the ensemble of tree models sequentially combines predictions of multiple tree models arranged in a sequence of models, a particular model in the sequence of models is trained using a first subset of training data, and
wherein during training, a prediction error from the particular model is passed to a succeeding model in the sequence of models, the succeeding model is trained using a second subset of training data and the succeeding model optimizes model weights based on the prediction error from the particular model to gradually reduce prediction errors and enhance prediction accuracy.
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