Machine and Deep Learning Technologies, Location Tracking and Obstacle Avoidance Algorithms, and Cognitive Wireless Sensor Networks in Intelligent Transportation Planning and Engineering
Machine and Deep Learning Technologies, Location Tracking and Obstacle Avoidance Algorithms, and Cognitive Wireless Sensor Networks in Intelligent Transportation Planning and Engineering

Summary/Abstract: Despite the relevance of machine and deep learning technologies, location tracking and obstacle avoidance algorithms, and cognitive wireless sensor networks in intelligent transportation planning and engineering, only limited research has been conducted on this topic. In this article, I cumulate previous research findings indicating that visual recognition tools, cloud computing algorithms, and spatial data analytics articulate urban driving environments. I contribute to the literature on object detection and tracking, vehicular communication technologies, and sensor fusion algorithms by showing that smart mobility technologies develop on computational object instantiation and recognition, sensor data processing algorithms, predictive simulation tools, and urban traffic modeling. Throughout March 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “intelligent transportation planning and engineering” + “machine and deep learning technologies,” “location tracking and obstacle avoidance algorithms,” and “cognitive wireless sensor networks.” As I inspected research published between 2021 and 2022, only 82 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 13, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, Distiller SR, and MMAT.

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