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IJOT
Published by Research Parks Publishing
ISSN : 26157071     EISSN : 26158140     DOI : https://doi.org/10.31149/ijot.v4i5
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology
International Journal on Orange Technologies (IJOT) is an online international peer-reviewed journal that publishes high-quality original scientific papers, short communications, correspondence, and case studies in areas of research, development, and applications of orange technology and engineering. Review articles of current interest and high standards may be considered. Only those manuscripts are considered for publication, the contents of which have not been published and are not being considered for publication in any other journal. The journal focuses on various learning and investigation areas to reach better excellence in research development as a whole.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 851 Documents
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An Analysis of The Utilization of Digital Twin and Big Data Analytics in Road Engineering for The Early Prediction of Urban Accident Hotspots Zulfadhli Zulfadhli; Defry Basrin; Rajib Muammar; Syarwan Syarwan
International Journal on Orange Technologies Vol. 8 No. 3 (2026): International Journal on Orange Technologies (IJOT)
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijot.v8i3.5835

Abstract

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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