<![CDATA[Purpose: Over Dimension Over Load (ODOL) violations in highway-based freight transportation in Indonesia contribute to infrastructure damage worth Rp43 trillion per year and rank as the second largest cause of national traffic accidents, yet existing monitoring remains partial, fragmented across agencies, and unable to support real-time, data-driven decisions. This study aims to advance understanding of multi-source data integration patterns, analytic design principles, and hierarchical interface architectures that together constitute a conceptual Decision Support System (DSS) framework for comprehensive ODOL surveillance in Indonesia. Research Design and Methodology: The method used is a Systematic Literature Review following the PRISMA protocol, sourced from Scopus, producing 30 indexed articles meeting inclusion criteria within 2022 to 2026. Findings and Discussion: The synthesis resulted in an architectural model, the Integrated ODOL Surveillance Dashboard (IOSD), comprising three functional layers: a multi-source data acquisition and integration layer; an analytics layer with violation detection, machine learning-based prediction, spatial analytics, and model interpretability modules; and an interactive visualization layer customized by user role. The model integrates Weigh in Motion, Automatic Number Plate Recognition, AI-based computer vision, and geographic information system data into a single real-time DSS platform spanning field operations to national policy formulation. Comparative insights from international heavy-vehicle monitoring practices further inform the model's institutional design. Implications: This research contributes a digital, accurate, cross-agency technical framework for ODOL supervision and opens a follow-up agenda for empirical validation of the IOSD prototype under real operational conditions within Indonesia's UPPKB network.]]>
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