<![CDATA[Smart cities require efficient traffic management to address congestion and optimize urban mobility. With increasing urban populations and vehicle vol- umes, traditional traffic control systems struggle to meet growing demands, ne- cessitating advanced technological interventions. This study aims to explore the integration of big data analytics and real-time data processing in optimizing urban traffic management. By leveraging machine learning algorithms, sensor data, and predictive models, this research seeks to enhance traffic flow and improve overall transportation efficiency. The methodology involves col- lecting data from traffic sensors, GPS-equipped vehicles, and surveillance cameras, which are then analyzed using Apache Hadoop and Apache Spark to derive meaningful insights. Real-time data processing techniques ensure im- mediate responses to traffic conditions, dynamically adjusting signal timings and rerouting vehicles to mitigate congestion. The results indicate a 15-25% reduc- tion in travel times in high-traffic areas where real-time adaptive signal control is implemented. Furthermore, the analysis highlights distinct traffic patterns, congestion hotspots, and travel time optimization opportunities that can sig- nificantly enhance urban transportation efficiency. This research confirms that big data-driven traffic management can lead to better decision-making, im- proved commuter experiences, and reduced environmental impact through lower emissions. Future studies should focus on advanced predictive algo- rithms, connected vehicle technology, and AI-driven automation to further refine urban traffic solutions. By implementing real-time analytics, smart cities can develop sustainable, efficient, and adaptive traffic management systems that improve mobility and quality of life for urban residents.]]>
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