<![CDATA[This study addresses the critical issue of escalating traffic accident rates in Indonesian urban areas, which recorded 101,904 cases as of September 2024. Current conventional methods for identifying accident-prone areas (blackspots) remain primarily reactive and lack the capability for early risk prediction. Conversely, while Digital Twin and Big Data Analytics technologies offer transformative potential for real-time road modeling, their integration into road engineering for the proactive prediction of accident blackspots in Indonesian urban contexts has not been systematically elaborated. This research employs a qualitative-descriptive library research approach, utilizing data sources that include academic books, scientific journal articles, and research reports. The collected data were analyzed using thematic content analysis techniques. The findings of this study reveal four primary conclusions. First, Digital Twin technology enables a virtual representation of road networks integrated with real-time sensor data, thereby creating a cyber-physical system capable of simulating diverse traffic scenarios. Second, Big Data Analytics possesses the capacity to process vast volumes of heterogeneous data including GPS, telematics, CCTV footage, and social media inputs to identify accident risk patterns that typically elude conventional statistical methods. Third, the integration of Digital Twin and Big Data Analytics facilitates the development of a risk prediction platform capable of proactively identifying accident blackspots with significantly higher accuracy than traditional approaches. Fourth, the implementation of these technologies in Indonesian urban areas encounters substantial challenges, notably limited data infrastructure, insufficient human resource capacity, and inadequate regulatory frameworks. This study concludes that the utilization of Digital Twin and Big Data Analytics in road engineering constitutes a highly promising strategy for the early prediction of urban accident blackspots. The implications of these findings underscore the necessity for targeted investments in data infrastructure, human capital development, and the formulation of regulatory frameworks that support the gradual and context-specific adoption of these technologies.]]>
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