PRISMA on Machine Learning Techniques in Smart City Development
Source abstract
Abstract
This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately, this work uniquely examines their intersection, highlighting the transformative potential of ML in urban development. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic and reproducible approach was employed to review 42 relevant studies. The analysis reveals four key themes: transportation and traffic optimization, people and event flow tracking, sustainability applications, and security use cases. These findings underscore ML’s ability to revolutionize smart city initiatives by enhancing efficiency, sustainability, and security. This review identifies significant research gaps and proposes future directions, positioning ML as a cornerstone in the evolution of intelligent urban environments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Cities and Technologies · Traffic Prediction and Management Techniques · Human Mobility and Location-Based Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Applied Economics and Quantitative Analysis, Faculty of Business and Administration, University of Bucharest, 030018 Bucharest, Romania
Faculty of International Business and Economics, Bucharest University of Economic Studies, 010374 Bucharest, Romania
DBA Program and Research Center, Geneva Business School, 1202 Geneva, Switzerland
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