Advancing green and digital logistics through spatio temporal data integration

Abstract

The integration of context-rich sources of freight transport data is necessary for devising data-grounded strategies to enhance green and digital logistics, yet largely challenged by three major aspects: data heterogeneity, cross-modal freight transport traceability, and real-time processing needs. This research presents a comprehensive framework to address these challenges through the principled development of AI solutions, complemented by key technologies, including blockchain for data integrity and IoT for real-time data collection across stakeholders. By standardizing and harmonizing diverse data sources, the proposed approach aims to improve decision-making and operational efficiency within logistics networks. The study includes a detailed roadmap that outlines strategic phases for implementing these technologies, with a case study of a Portuguese port illustrating the practical benefits of the framework. The findings highlight the critical role of integrated data systems for monitoring intermodality patterns, reducing emissions, optimizing routes, and fostering collaboration among stakeholders, ultimately advancing sustainability and efficiency of logistics and supply chain systems.