Enhancing UAV Navigation: Predictive modeling of metro station locations based on spatial analysis and machine learning techniques

Determining the locations of metro stations transcends mere convenience; it fundamentally influences urban development, vitality, and connectivity. Metro stations are essential for alleviating traffic congestion, reducing travel durations, and enhancing public transport accessibility for a larger population. In swiftly growing urban areas, particularly in a nation such as India, identifying optimal locations for metro stations poses significant challenges. This study illustrates how the integration of machine learning and urban planning might result in more intelligent and sustainable cities. It connects data with decision-making, addressing the intricacies of urban expansion, metropolitan planning, and UAV-based spatial analysis. The proliferation of Unmanned Aerial Vehicles (UAVs) for urban logistics, surveillance, and passenger transport necessitates the optimization of metro station placements in new cities. This study provides a methodology for forecasting metro station placements in emerging cities, facilitating both terrestrial and aerial mobility planning. The research entails the generation and analysis of a real-world dataset, utilizing machine learning methodologies to forecast metro station locations. Experiments utilizing multi-city data across diverse categorization approaches confirm the efficacy of the suggested strategy, with prospective UAV-assisted spatial visualization.