Equity-based spatial clustering of unmet DRT demand for fixed-route transit optimization in underserved areas

This study presents a spatially explicit framework for identifying and prioritizing unmet mobility demand for Demand-Responsive Transit (DRT) in Kalamazoo County, Michigan. By overlaying fixed-route transit stops with DRT origin–destination data, the analysis reveals persistent accessibility gaps, particularly in suburban and rural areas. A buffer sensitivity analysis shows that 22.9% of trip origins and 21.9% of destinations fall outside the standard 0.5-mile walkability threshold, highlighting structural first- and last-mile disconnects. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) identifies 27 clusters of unserved demand. These clusters are ranked using a composite Priority Score Index incorporating trip density and socio-demographic indicators, including income, poverty, and disability prevalence. High-ranking clusters exhibit both concentrated unmet demand and elevated social vulnerability. Temporal analysis indicates that 82% of unserved trips occur on weekdays, with peaks during commuting hours. The findings demonstrate the limitations of fixed-route systems in dispersed environments and provide an equity-informed framework to guide targeted DRT deployment. The proposed methodology offers a replicable and scalable approach for transit agencies seeking to integrate demand-responsive services into hybrid transit systems.