Big Data-driven Governance of Smart Sustainable Intelligent Transportation Systems: Autonomous Driving Behaviors, Predictive Modeling Techniques, and Sensing and Computing Technologies
Andrej Novak1, Alena Novak Sedlackova1, Marek Vochozka2, and Gheorghe H. Popescu3ABSTRACT. This article reviews and advances existing literature concerning com- puter vision and route planning algorithms, sensing and computing technologies, and vehicle and pedestrian detection tools. Throughout June 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “big data-driven governance” + “smart sustainable intelligent transportation systems” + “autonomous driving behaviors,” “predictive modeling techniques,” and “sensing and computing technologies.” As research published in 2022 was inspected, only 181 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, we selected 37 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR.
Keywords: smart sustainable intelligent transportation system; autonomous driving behavior; predictive modeling technique; sensing and computing technology
How to cite: Novak, A., Novak Sedlackova, A., Vochozka, M., and Popescu, G. H. (2022). “Big Data-driven Governance of Smart Sustainable Intelligent Transportation Systems: Autonomous Driving Behaviors, Predictive Modeling Techniques, and Sensing and Computing Technologies,” Contemporary Readings in Law and Social Justice 14(2): 100–117. doi: 10.22381/CRLSJ14220226.
Received 25 July 2022 • Received in revised form 22 November 2022
Accepted 27 November 2022 • Available online 30 November 2022