ABSTRACT. The aim of this systematic review is to synthesize and analyze geolocation data mining and tracking, simulation modeling and ambient scene detection tools, and metaverse and immersive technologies. With increasing evidence of geolocation data processing algorithms, holographic imaging and multisensor fusion technologies, and visual imagery and virtual navigation tools, there is an essential demand for comprehending whether visual and spatial intelligence tools, biometrics data fusion, and metaverse and immersive technologies enable decentralized 3D digital worlds and synthetic simulation environments. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout June 2023, with search terms including “3D immersive environments” + “urban simulation and modeling technologies,” “smart city digital twins,” and “predictive control and sensor fusion algorithms.” As research published between 2022 and 2023 was inspected, only 153 articles satisfied the eligibility criteria, and 20 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: Abstrackr, ASReview Lab, Catchii, Colandr, Nested Knowledge, and SluRp.

Keywords: urban simulation and modeling; smart city digital twin; predictive control; sensor fusion; 3D immersive environment

How to cite: Lăzăroiu, G., Kliestik, T., Dengov, V. V., and Tulyakova, I. R. (2023). “Urban Simulation and Modeling Technologies, Smart City Digital Twins, and Predictive Control and Sensor Fusion Algorithms in 3D Immersive Environments,” Geopolitics, History, and International Relations 15(2): 9–23. doi: 10.22381/GHIR15220231.

Received 12 July 2023 • Received in revised form 23 October 2023
Accepted 26 October 2023 • Available online 30 October 2023