<![CDATA[Traffic congestion is a major issue in urban areas, leading to reduced time efficiency, higher fuel consumption, and increased exhaust emissions. This study aims to design and implement a traffic congestion level prediction system to optimize vehicle movement using Dijkstra's Algorithm. The algorithm is utilized to determine the shortest and fastest routes dynamically based on travel time weights and road segment density levels. The research methodology includes route data collection, road network graph modeling, pathfinding algorithm implementation, and system performance testing under various traffic density scenarios. The results demonstrate that Dijkstra's Algorithm is effective in recommending alternative routes that minimize travel time and avoid severe congestion bottlenecks, thereby significantly improving urban mobility efficiency. ]]>
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