AbstractThe transfer between bus and metro is an integral part of the public transportation system, but accurately identifying transfer patterns and real-time transfer demands at each metro station for different transfer modes remains a vital issue. Existing identification methods are based mainly on a one-size-fits-all fixed transfer time threshold, or dynamic transfer identification methods only for the metro-to-bus mode, which is not sufficient for mining the hourly transfer characteristics of different transfer modes at each station. This study combined bus smart card (SC) data and the corresponding bus Global Positioning System (GPS) track data into one data set to infer the bus boarding stops. A new algorithm to identify transfer characteristics based on two rules about dynamic transfer time threshold and distance threshold is proposed for the metro-to-bus mode. A new method to recognize transfer patterns based on dynamic transfer time thresholds is presented for the bus-to-metro mode. The two proposed transfer recognition methods were validated using survey data from Shenzhen, China. The results show that the distribution of transfer time by the proposed methods is consistent with that of the survey data. The p-values of Mann–Whitney U tests of transfer data identified by the method and survey data all were greater than 0.05. This indicates that the methods proposed in this study can identify effectively the transfer patterns at each metro station per hour of the day, with good generalizability. Moreover, the proposed methods can explain transfer patterns and hourly transfer demand at each station for different transfer modes better than other methods.