Autonomous Reskilling Systems in Transportation Workforce: A Framework for Digital Transformation Era

Rapid advances in autonomous technologies and artificial intelligence (AI) are transforming the transportation sector and redefining workforce requirements. As automation becomes increasingly embedded in transportation operations, a growing gap has emerged between existing worker competencies and the skills needed to supervise, manage, and interact with intelligent systems. Addressing this challenge requires reskilling approaches that are continuous, adaptive, and responsive to technological change. This study proposes a conceptual framework for an Autonomous Reskilling System (ARS) to support sustainable workforce transition in the transportation sector. A systematic literature review of academic publications, policy reports, and industry studies indexed in Scopus between 2015 and 2025 was conducted. The review identifies four critical dimensions influencing effective reskilling initiatives: technology readiness, organizational capacity, human capital development, and economic sustainability. These dimensions are integrated into a three-layer framework consisting of an input layer, an AI-driven processing engine, and an output layer delivering personalized learning and career transition pathways. The proposed framework contributes to both theory and practice by offering a structured and scalable model for workforce development in the era of digital transformation. It provides guidance for organizations, policymakers, and educational institutions seeking to mitigate workforce displacement while enhancing long-term employability in automated transportation systems.