ABSTRACT. The present study systematically reviews the existing research on big geospatial data analytics, connected vehicle technologies, and visual perception and sensor fusion algorithms in smart transportation networks. My findings indicate that deep reinforcement learning techniques and location tracking algorithms decrease traffic collisions and fatalities. I contribute to the literature by clarifying that cognitive artificial intelligence and collision avoidance algorithms, simulation and virtualization technologies, and geospatial mapping tools further self-driving car acceptance and adoption. Throughout March 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “smart transportation networks” + “big geospatial data analytics,” “connected vehicle technologies,” and “visual perception and sensor fusion algorithms.” As research published between 2019 and 2022 was inspected, only 95 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 14 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

Keywords: connected vehicle technologies; visual perception; sensor fusion

How to cite: Peters, E. (2022). “Big Geospatial Data Analytics, Connected Vehicle Technologies, and Visual Perception and Sensor Fusion Algorithms in Smart Transportation Networks,” Contemporary Readings in Law and Social Justice 14(1): 73–88. doi: 10.22381/CRLSJ14120225.

Received 18 March 2022 • Received in revised form 14 July 2022
Accepted 27 July 2022 • Available online 30 July 2022

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