A Comparative Analysis of Intelligent Transportation System Approaches for Urban Traffic Management
Keywords:
Intelligent Transportation Systems, Urban Traffic Management, Traffic Signal Control, Real-time Data Analytics, Sustainable Transportation, Congestion Mitigation, Smart CitiesAbstract
Urban traffic congestion is a prevailing global issue, leading to increased travel times, environmental degradation, and economic losses. This study employs a comparative analysis of various Intelligent Transportation System (ITS) frameworks, utilizing both qualitative metrics and quantitative data gathered from 15 urban areas in the United States between 2022 and 2023. The research identifies key performance indicators (KPIs) such as traffic flow efficiency, reduction in average vehicle delay, and emission levels. Our findings reveal that Advanced Traffic Signal Control Systems (ATSCS) outperform conventional timing solutions, achieving a 25% increase in traffic throughput with a p-value of <0.001. Additionally, the integration of real-time data analytics into traffic management systems enhances operational efficiency by 30%. This study not only provides empirical evidence of the most effective ITS methodologies but also contributes to policy formulation for smart city initiatives.
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