Multi-Sensor Fusion Technology, Spatial Simulation and Environment Mapping Algorithms, and Real-World Connected Vehicle Data in Smart Sustainable Urban Mobility Systems
Multi-Sensor Fusion Technology, Spatial Simulation and Environment Mapping Algorithms, and Real-World Connected Vehicle Data in Smart Sustainable Urban Mobility Systems

Summary/Abstract: The aim of this systematic review is to synthesize and analyze multi-sensor fusion technology, spatial simulation and environment mapping algorithms, and real-world connected vehicle data in smart sustainable urban mobility systems. With increasing evidence of autonomous vehicle planning algorithms, object localization and mapping tools, and spatial computing technology, there is an essential demand for comprehending whether road anomaly detection tools, blockchain and data fusion technologies, and trajectory estimation algorithms assist smart traffic planning and analytics. In this research, prior findings were cumulated indicating that automated collision avoidance systems, data monitoring algorithms, and virtual navigation tools reduce crash severities. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March 2022, with search terms including “smart sustainable urban mobility systems” + “multi-sensor fusion technology,” “spatial simulation and environment mapping algorithms,” and “real-world connected vehicle data.” As we analyzed research published between 2019 and 2022, only 88 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 13, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Meth-odological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR.

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