As fare and data collection technology has developed, the resolution of collected data has reached the level of the individual traveler in investigations of transit passenger behavior. This paper investigates the use of these data to estimate passenger origins and destinations at the level of individual stops. Because of a lack of information from the fare collection system, researchers still need some estimate of passengers' alighting stops to complete each passenger trip chain on a specific day. Automated fare collection (AFC) and automated vehicle location (AVL) systems are the inputs to the estimation. Instead of typical AVL data, the paper proposes two models to estimate the alighting stop; both consider passenger trip chaining by using AFC data, transit schedule data (Google's General Transit Feed Specification), and automated passenger counter (APC) data. The paper validates the model by comparing the output to APC data with vehicle location data (APC-VL) and performs sensitivity analyses on several parameters in the models. To detect transfer trips, the new models propose a submodel that takes into account the effect of service headway in addition to some typical transfer time thresholds. Another contribution of this study is the relative relaxation of the search in finding the boarding stops, which enables the alternative algorithm to detect and fix possible errors in identification of the boarding stop for a transaction. As a result, the paper provides algorithms for the proposed models and sensitivity analysis for several predefined scenarios. The results are based on data and observed bus passenger behavior in the Minneapolis–Saint Paul, Minnesota, area.