Automated ETA Prediction for Freight Rail Containers for a Global Railcar Manufacturer

Business Objective

Our Client is one of the leading global providers of systems and components for freight and transit railcars and locomotives. They provide live freight car locations to the end clients.

The client wanted to improve customer service by developing an automated solution to predict the estimated arrival time of freight containers through near real-time estimates on arrival/estimated delay as the rail container moves on its route based on historical data.

Solution Methodology

Input data for the model included Geolocation pings from various trips containing tracking Device ID, Latitude/Longitude, Geofence/City, timestamp, motion status, and Geofence type
Identified origin and destination pairs based on geofence type. Grouped containers from the same train into one trip by offsetting time stamp
Engineered 55 features including seasonal variables, current trip variables, and historical variables
Identified key input features -distance left, historical travel & dwell time, origin time & day, and developed model leveraging XGBoost regression model
Productionized model with an automated process to predict ETA for live trips. Model handled all possible exceptions such that the module is break-free. Logged every step for error-handling

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Automated ETA Prediction for Freight Rail Containers for a Global Railcar Manufacturer

Business Objective

Challenges

Solution Methodology

Business Impact

Automated ETA Prediction for Freight Rail Containers for a Global Railcar Manufacturer

Business Objective

Challenges

Solution Methodology

Business Impact

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