<![CDATA[This research analyzes the distribution of public transport services from AKDP and AKDK units in 40 sub-districts in Bogor Regency, an area with rapid population growth that faces congestion due to dependence on private vehicles. The main problem is that service gaps have not been mapped quantitatively, so the aim of this research is to apply clustering techniques to identify underserved areas and provide an objective basis for data-based policy recommendations. The proposed method is a quantitative approach using the K-Means Clustering algorithm on secondary datasets from the Department of Transportation with three main features: number of licensed fleets, number of unlicensed fleets, and total fleet. The main findings succeeded in classifying sub-districts into three clusters, Cluster 0 (established services), Cluster 1 (static growth), and Cluster 2 (significant service gaps dominated by informal fleets). In conclusion, this research proves the existence of significant service disparities, and shows that the clustering method is effective in mapping priority zones for more equitable transportation policy interventions.]]>
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