Elenna Dugundji, Massachusetts Institute of Technology

Abstract

This study employs a calibrated discrete-event simulation model to evaluate the operational impacts of modal shift interventions at the Port of New York/New Jersey. Building upon a previously published methodological framework, we conduct comprehensive baseline and scenario analyses to quantify how changes in truck-rail modal split affect port congestion, container dwell times, and yard utilization. Systematic scenario testing across rail adoption rates from 5% to 40%, combined with variations in train frequency and capacity, demonstrates that strategic modal shifts can reduce average container dwell time by over 10 hours. However, beyond 25% adoption without infrastructure scaling, rail itself becomes a bottleneck. These findings provide actionable insights for port authorities and policymakers considering intermodal interventions, demonstrating that existing rail infrastructure possesses untapped capacity that, when strategically utilized, can improve port performance.

The work to be described was joint with Kevin Power, Yassine Lahlou-Kamal, Nikolay Aristov, Thomas Koch.

Bio:

Elenna Dugundji is Director of the Deep Knowledge Lab for Supply Chain and Logistics and a Research Scientist at the MITCenter for Transportation and Logistics. shapes Supply Chain futures by bringing expertise in demand forecasting, machine learning and AI to research in mainport logistics, involving Network analytics, Optimization of operational processes, Tactical planning and Strategic asset management.