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Abstract

Smart cities have solved a number of big problems that have come up with traditional urbanization around the world. Nevertheless, sustainable traffic management in smart cities has garnered insufficient attention from academics owing to its intricate and varied characteristics, which directly impact the transportation systems of smart cities. The research sought to tackle traffic-related challenges in smart cities by developing a sustainable framework utilizing Internet of Things (IoT) and Intelligent Transportation System (ITS) technologies. Furthermore, the two methodologies employed real-time traffic data and gathered information about vehicles and road users using AI sensors and ITS-based devices. The data may be analyzed and communicated via machine learning techniques and cloud computing for traffic management, decision-making policies and future documentation. The proposed framework indicates that implementing such systems in smart city transport could significantly enhance traffic outcome prediction, traffic forecasting, traffic decongestion, reduction of road users' lost hours, provision of alternative routes and simplification of urban transport for residents. The proposed integrated framework may effectively tackle pollution concerns in smart cities by enhancing public transit and supporting low-carbon emission zones. The implementation of these solutions help smart cities to attain sustainable traffic management and diminish their carbon impact, becoming them habitable and ecologically sound.