Smart resilient transportation architectures as enablers of supply chain continuity

Abstract

Transportation systems are increasingly exposed to climate-related, operational and cyber disruptions that threaten the continuity of supply chains. Although smart transportation technologies enhance efficiency and visibility, they do not inherently ensure resilience. This study aims to develop and refine a smart resilient transportation architecture that explains how artificial intelligence (AI) and adaptive capacity can be systematically integrated to enable supply chain continuity under disruption conditions. This study adopts a conceptual and systems-based design approach grounded in smart transportation systems and transportation resilience research. A multilayer architecture is developed that integrates sensing and data acquisition, AI-driven intelligence, adaptive control mechanisms and resilience capabilities. This study shows that resilience emerges from architectural integration rather than isolated smart technologies. AI-enabled prediction and learning enhance adaptive capacity only when embedded within coordinated control mechanisms such as dynamic routing, intermodal switching and resource reallocation. The interaction of sensing, intelligence and adaptive control strengthens absorptive, adaptive and recovery capacities of transportation systems, thereby stabilizing physical flows and enabling supply chain continuity in the presence of disruptions. This study develops a smart resilient transportation architecture that integrates sensing, AI-enabled intelligence, adaptive control, governance and resilience capabilities within a unified framework. The architecture explains how transportation systems can anticipate, respond to and recover from disruptions while maintaining logistics flows. By linking transportation resilience to supply chain continuity, this study provides a transportation-centric perspective that extends existing smart transportation and resilience research.