IoT-Based Urban Mobility Solutions for Traffic Congestion
DOI:
https://doi.org/10.48314/ceti.vi.44Keywords:
Edge computing, Internet of things, Smart city, Artificial intelligence optimization, Data privacy, Real-time analyticsAbstract
Urban traffic congestion presents a significant challenge in contemporary cities, affecting economic efficiency, environmental health, and overall quality of life. The swift growth of urban populations and the rise in automobile use have burdened conventional traffic management systems, which frequently fall short in adapting to real-time conditions. This congestion prolongs travel times and increases fuel consumption, air pollution, and economic losses due to reduced productivity.
This study investigates how the Internet of Things (IoT) can tackle these issues by facilitating adaptive, data-informed traffic management strategies. By incorporating real-time data gathering, edge computing for prompt local analysis, and Artificial Intelligence (AI)-driven enhancements, IoT offers a robust foundation for contemporary traffic regulation. The paper reviews the present landscape of IoT-based systems, suggests a multi-tiered framework for managing congestion, and examines practical examples that demonstrate the efficacy of IoT solutions in minimizing delays and boosting urban transportation. Furthermore, it tackles challenges such as data security, scalability, and the integration with existing systems. It also looks into emerging trends like self-driving cars and 5G technology, which are expected to further strengthen IoT's contribution to sustainable urban traffic management.
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