Integrating artificial intelligence and blockchain for the resilience of sustainable multimodal transport: A systematic review

This article investigates the integration of Artificial Intelligence and blockchain within multimodal transport, with the objective of assessing their combined contribution to resilience and sustainability, while proposing an integrative conceptual model. The methodology relies on a PRISMA 2020–compliant systematic literature review covering 2019–2024. Searches were performed in Scopus, Web of Science, Cairn, IEEE Xplore, and ScienceDirect for peer-reviewed journal articles, reviews, and conference papers published in English, French, or Spanish. Titles/abstracts and full texts were screened independently by two reviewers; data were extracted using a pilot-tested coding sheet; and study quality was appraised using JBI (quantitative), CASP (qualitative), and MMAT (mixed methods). Evidence was synthesized through a SWiM-oriented thematic narrative approach and systematically reported in three evidence categories (AI-only, Blockchain-only, and AI+Blockchain). The analysis focused specifically on multimodal transport, examining the interplay between AI, blockchain, resilience, and sustainability. The review includes eighty-three studies and indicates a marked growth in publications from 2019 onward, with geographical predominance in Europe, Asia, and North America, and a prevalence of exploratory qualitative approaches centered on case studies. Four thematic axes emerge: AI’s contributions to optimization, adaptive responses, and prediction; blockchain’s role in traceability and smart contracts; joint integration logics; and conceptual frameworks of resilience and sustainability. The proposed model establishes a connection between AI–blockchain capabilities and organizational mediators such as visibility, coordination, trust, and automation. These mediators exert a direct influence on resilience dimensions, which include anticipation, absorption, adaptation, and acceleration, as well as on sustainability dimensions that encompass economic, environmental, and social aspects. The entire framework is shaped by contextual factors, notably interoperability, governance, skills, and the institutional environment. This study exposes the limits of the current literature, characterized by a lack of quantitative evidence and limited transferability, and outlines a future research agenda based on mixed methods and validated instruments. It advocates gradual adoption trajectories while emphasizing the need for strengthened data governance and the development of interoperability standards.