Abstract: The technology disclosed relates to predicting an estimated time of arrival for an equipment via a railroad. In particular, the technology disclosed relates to 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 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.

Abstract: Systems and methods are disclosed for predicting an estimated time of arrival for an equipment at a destination location via a railroad network. The method includes 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. The method includes 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. The trained machine learning model can use an ensemble of tree models for the predicting of the estimated time of arrival of the equipment at the destination location. The ensemble of tree models can sequentially combine predictions of multiple tree models arranged in a sequence of models.

Abstract: Systems and methods for predicting an estimated time of arrival for an equipment at a destination location are disclosed. The method includes inputting to a trained machine learning model a starting location, the destination location and at least one event data, generated in response to an event during a trip of the equipment via the railroad from the starting location to the destination location. The machine learning model is trained using a plurality of historical shipment route data. A particular historical shipment route data comprises at least a location and a corresponding timestamp. The method includes predicting, using the trained machine learning model, the estimated time of arrival of the equipment at the destination location.