Abstract
In light of rapid urbanization and escalating traffic congestion, the smart city concept is vital for fostering sustainable urban development. A significant challenge in urban management is the monitoring of transport infrastructure while considering its environmental impact. This article explores the integration of environmental sensor data and artificial intelligence (AI) techniques to enhance the efficiency of urban transport system management.It focuses specifically on the deployment of sensor networks that gather data on air quality, noise levels, and other environmental indicators associated with transport activities. Utilizing machine learning and big data analysis, the study processes diverse information, identifies correlations between traffic flows and environmental factors, and forecasts potential traffic congestion and environmental hazards.The paper introduces a proposed architecture for an intelligent monitoring system that consolidates data from environmental sensors and transport systems into a cohesive analytical framework. The results suggest that AI-driven transportation methods improve monitoring accuracy, facilitate timely identification of adverse environmental trends, and aid management decision-making within smart cities. Ultimately, the findings validate the potential of AI in establishing sustainable and environmentally friendly transportation systems.