AN IN-DEPTH BIBLIOMETRIC ANALYSIS OF PREDICTION IN MICROMOBILITY

Abstract

Traffic, personal life requirements, pandemic, sustainability, cost & time efficiency are only a few major reasons of proliferation of micromobility. Especially in urban life, usage of shared & self-owned e-scooters, e-bikes, small cars are increasing day by day. With the growth of the market, more data occurs to work on; usage statistics, vehicle&battery data, legal regularities, injuries and etc. With the help of central management systems of big vehicle fleets, municipalities and big data portals, accessing data gets easier. Because of the high-quality big amount of data, it is critical to analyze the data accurate for business management and public administration by the scientific researchs. In last decade applications of advanced prediction methods on micromobility got high attention from the researchers. This study aims to analyze the existing work in the literature through bibliometric analysis. Existing literature is gained by searching online scientific publications with a systematic keyword search. And the literature data was analyzed and interpreted by different point of views via an academic software application.