Geospatial Data Mining, Computer Vision-based Lane Detection and Object Localization Algorithms, and Mapping and Navigation Tools in Smart Urban Mobility Systems
Geospatial Data Mining, Computer Vision-based Lane Detection and Object Localization Algorithms, and Mapping and Navigation Tools in Smart Urban Mobility Systems

Summary/Abstract: In this article, I cumulate previous research findings indicating that networked transport systems, autonomous mobility technologies, and multi-sensor environmental data fusion typify urban driving environments. I contribute to the literature on geospatial data mining, computer vision-based lane detection and object localization algorithms, and mapping and navigation tools in smart urban mobility systems by showing that big geospatial data analytics, mobility simulation tools, and sensor data processing algorithms shape vehicular communication technologies. Throughout April 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “smart urban mobility systems” + “geospatial data mining,” “computer vision-based lane detection and object localization algorithms,” and “mapping and navigation tools.” As I inspected research published between 2019 and 2022, only 93 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 15, 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, ROBIS, and SRDR.

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