Networked Transport Systems: Spatial Cognition Algorithms, Mobility Data Processing Tools, and Deep Learning-based Sensing Technologies
Networked Transport Systems: Spatial Cognition Algorithms, Mobility Data Processing Tools, and Deep Learning-based Sensing Technologies

Summary/Abstract: The present study systematically reviews the existing research on geospatial mapping and simulation modeling tools, environment and vehicle sensors, and crash avoidance technologies. My findings clarify that interconnected sensor networks integrate visual recognition and image processing tools, smart mobility technologies, and trajectory tracking control algorithms, and I contribute to the literature by indicating that connected autonomous vehicles necessitate urban traffic modeling and data visualization tools, deep neural network technology, and predictive control algorithms. Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “autonomous vehicle ethics” + “networked transport systems” + “spatial cognition algorithms,” “mobility data processing tools,” and “deep learning-based sensing technologies.” As research published in 2022 was inspected, only 185 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 36 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.

• Details
• Contents