Abstract: A method for using a trained machine learning (ML) model to determine an ETA of a railcar to a destination and to generate a fleet readiness alert, including: collecting railcar trip information; collecting coordinates of a rail station associated with a rail carrier; creating a joined dataset by combining the trip information and coordinates; performing feature engineering (FE) on the joined dataset to create a feature engineered output including a plurality of engineered features; combining the joined dataset with the feature engineered output to create ML model feature inputs for the trained ML model; and activating the ML model, which accepts the model feature inputs as an input, determines the ETA of the railcar, and generates the fleet readiness alert based on the determined ETA of the railcar, indicating at least a usability status of a fleet of railcars at the depot yard within a fixed time.

Abstract: A method for using a trained machine learning (ML) model to determine an ETA of a railcar to a destination and to generate a fleet readiness alert, including: collecting railcar trip information; collecting coordinates of a rail station associated with a rail carrier; creating a joined dataset by combining the trip information and coordinates; performing feature engineering (FE) on the joined dataset to create a feature engineered output including a plurality of engineered features; combining the joined dataset with the feature engineered output to create ML model feature inputs for the trained ML model; and activating the ML model, which accepts the model feature inputs as an input, determines the ETA of the railcar, and generates the fleet readiness alert based on the determined ETA of the railcar, indicating at least a usability status of a fleet of railcars at the depot yard within a fixed time.