Machine Learning-Driven Cost Prediction in Intermodal Logistics: A Case Study of Porto Romano

Abstract

As global supply chains continue to expand in complexity and scale, the integration of intelligent, data-driven solutions within logistics operations becomes increasingly essential. Ports are evolving from traditional gateways into dynamic, technology-enabled hubs that demand advanced predictive capabilities and operational flexibility. With Porto Romano poised to become a major logistics and energy platform for Albania, the early adoption of machine learning (ML) tools is a strategic advantage. This study explores the application of two widely recognized ML algorithms- Random Forest Regression and Support Vector Machine (SVM) Regression- to estimate intermodal transport costs based on critical factors such as distance, travel time, cargo weight, and transport mode. A dataset comprising 250 transport cases was constructed to evaluate model performance using metrics such as Mean Squared Error (MSE), R² Score, Accuracy, and F1-Score. The data for the railway sector were simulated to support future port development, while the maritime and road sector data were derived from actual observations. Results indicate that Random Forest consistently outperformed SVM in predictive accuracy and robustness. Feature importance analysis further highlighted distance and travel time as the primary drivers of transport cost variations, aligning with operational expectations. By integrating ML-based cost prediction tools, Porto Romano could significantly improve its logistical efficiency, cost-effectiveness, and strategic competitiveness in the Balkan region. Moreover, this research underscores the broader value of machine learning in optimizing global intermodal logistics networks, offering pathways towards more sustainable and resilient supply chains. Future work will aim to refine these models with live operational data and expand into deep learning methodologies for even greater predictive capabilities