Graph-Based Analysis for Optimizing Traffic Flow in Urban Networks

The increasing urban population and vehicle density have led to significant traffic congestion issues, affecting both environmental sustainability and the commuter experience. This chatper presents a comprehensive approach to optimizing traffic flow using graph-based analysis in urban networks. By modeling urban traffic networks as weighted graphs, where nodes represent intersections and edges denote road segments, graph theory and machine learning techniques applied to analyze traffic dynamics, identify bottlenecks, and suggest optimal routing strategies. Algorithms are explored to improve traffic efficiency. Furthermore, real-time data integration from IoT-enabled sensors and GPS devices allows for dynamic traffic management and responsive routing solutions. This study highlights the potential of graph-based analysis to advance smart city initiatives and pave the way for more sustainable and intelligent urban traffic systems.