Comprehensive Analysis of Taxi Service Distribution, Demand Hot spots, and Air Pollution Impact: A GIS and Big Data Approach

Abstract

As urban transportation demand increases, optimizing taxi services is critical for improving traffic flow and reducing environmental impact. This study investigates the spatial distribution of taxi services, demand hotspots, and their relation to air pollution using Geographic Information Systems (GIS), big data, and land use analysis. After collecting and processing taxi GPS data, we identified high-demand areas such as airports and commercial centers. We then overlaid this data with air pollution monitoring information to analyze potential correlations. The analysis revealed significant overlaps between regions of frequent taxi operation and elevated pollution levels. Using spatio-temporal models, we predicted demand patterns across different periods and explored the connection between land use (commercial, residential, and industrial zones) and traffic congestion. The results show that taxi services contribute significantly to air quality deterioration in densely populated and commercially active areas. These findings suggest that optimized taxi dispatch and fleet redistribution strategies could not only enhance operational efficiency but also alleviate air pollution in urban centers. This study provides actionable insights for policymakers and taxi companies to develop data-driven approaches to urban transportation management, reducing air pollution while improving service efficiency.