<![CDATA[Urban traffic congestion has emerged as a significant challenge, primarily driven by rapid urban expansion and increasing vehicle usage. This study presents the development of a congestion-prone point classification system utilizing the Self-Organizing Maps (SOM) algorithm, integrated into an android-based mobile application. The primary objective is to facilitate the real-time detection and visualization of traffic density hotspots using unsupervised machine learning techniques. Traffic-related data comprising vehicle volume, type distribution, and geospatial coordinates are systematically collected, preprocessed, and transformed into multidimensional feature vectors. These vectors are processed using the SOM algorithm to uncover latent congestion patterns across various road segments. Testing results indicate that the proposed model is capable of accurately identifying congestion-prone areas, which are subsequently visualized within the mobile application using a colour-coded map interface. This integration provides commuters and traffic management authorities with actionable, data-driven insights to support route optimization and congestion alleviation strategies. Overall, the proposed system contributes to the advancement of intelligent transportation infrastructure within the broader framework of smart city development.]]>
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