Harnessing AI for Sustainable Traffic Management: Enhancing Efficiency, Safety, and Mobility in Smart Cities
Abstract
With urbanization and increasing vehicular congestion, effective traffic management has become a critical challenge for cities worldwide. Artificial Intelligence (AI) offers promising solutions to address these issues and create sustainable transportation systems. This paper explores the role of AI in traffic management, focusing on its potential to enhance efficiency, safety, and mobility in smart cities. We delve into various applications of AI in traffic management, including traffic flow prediction, congestion detection, intelligent signal control, and dynamic route guidance. Moreover, we discuss the use of AI-enabled technologies such as computer vision, machine learning, and data analytics to extract valuable insights from real-time traffic data. The paper highlights the environmental benefits of AI in traffic management, such as reducing carbon emissions through optimized traffic flow and promoting the adoption of electric vehicles. We also address ethical considerations and privacy concerns associated with AI implementation in transportation systems. Furthermore, we explore the importance of stakeholder collaboration, data sharing, and policy frameworks to ensure responsible and inclusive AI adoption in traffic management. By leveraging AI capabilities, cities can optimize traffic operations, improve safety, and enhance transportation efficiency, leading to reduced congestion and enhanced quality of life for residents. This paper provides insights and recommendations for urban planners, policymakers, and transportation authorities to embrace AI-driven approaches in traffic management, fostering sustainable and future-ready cities.
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