AI Innovation
Princeton develops and deploys AI, Optimization, and Machine Learning solutions in Rail and other asset-based transportation networks and facilities. Global interest in AI is white-hot, and executives trust us to build new tools to advance functionality and outcomes, and to integrate emerging technologies into existing systems.
Case Study
AI Innovation in Rail: Digital Twin
Princeton utilizes artificial intelligence and a next-generation intermodal digital twin to transform how a Class I Railroad manages its terminal operations—unlocking innovative solutions and setting a new benchmark for the future of rail.
The AI-powered digital twin enables the railroad to:
Provide deep insight into operational needs, empowering data-driven decisions across the rail network
Test, validate, and fine-tune optimization models in a realistic, dynamic environment without disrupting live operations
Explore many different scenarios and possibilities at a fraction of the cost of physical testing
Harness data collection to support predictive maintenance and proactive planning
Case Study
Rail AI Innovation: Transforming Revenue Forecasting
A Class I railroad sought a new-generation revenue forecasting system, leveraging cutting-edge technologies, data collection, and advanced analytics to address evolving demands in the rail industry. Princeton developed a flexible, scenario-based forecasting tool powered by macroeconomic indicators—eliminating the need for resource-heavy, bottom-up forecasting methods.
“Princeton Consultants proved to be a great choice for this [Revenue Forecasting] project. They remained very aware of our timeline needs and were helpful in making sure we built what was needed to meet the initial requirements, while also keeping in mind future needs. Their guidance enabled us to deliver the project on time.”
– Class 1 Rail AVP Market Research and Forecasting
Case Study
AI Innovation: Transportation Network Optimization
UPS set out to build a next-generation logistics network—smart, flexible, integrated, and global. Princeton partnered with in-house teams to develop the core simulation capability for its Network Planning Tool (NPT), focused on optimizing linehaul operations.
Key outcomes of the NPT include:
Efficient routing and re-routing of package flows using advanced AI algorithms
Volume shifting to lower-cost modes like rail transport, improving margins without impacting service
Annual cost savings of $100 million to $200 million