Demand responsive bus has good social benefits in solving traffic congestion and environmental pollution caused by the increase of private cars. This study takes demand responsive transit as the research object and studies the characteristics of traveler space, application scenarios, line station pattern and other aspects. Based on a few domestic and foreign operation cases, the study determines common application scenarios and service models. With DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, the research extracts the potential routes of demand responsive transit, which can depict the travel characteristics of different groups of people in order to promote their matching with application scenarios and summary of potential routes. Finally, taking Beijing as an example, the study proves that the relevant theoretical methods proposed in this study can effectively assist the demand responsive public transport planners to extract potential routes, analyze the selection of application scenarios and service modes, so as to have good theoretical and practical significance.

CICTP