Efficient design of express and local bus services in urban corridors requires accurate understanding of the travel demand and heterogeneities in passengers’ preferences and needs. Public transit Automated Fare Collection (AFC) systems provide a high-coverage source of data that facilitates an unprecedented opportunity for understanding the demand patterns and passenger preferences for more efficient service designs.In this paper, a Bayesian inference method is proposed to analyze the AFC repeated boarding records of passengers in the presence of real-time bus arrival information. A continuous representation of boarding strategies is introduced that can capture the behavior of passengers if they extend their waiting times to board a preferred route that is due shortly. The proposed method is tested in a case study on the Western Avenue corridor in Chicago, Illinois. The case study demonstrates the possibility of making confident inferences (95%) for thousands of the corridor passengers. The case study also confirms intuitive correlation of the inferred strategies with variables such as travel distance, egress distance, time of day, and availability of countdown timers at the stop. Potential biases of the inference sample and possible applications in service planning are discussed.