## 1 Introduction  
Transfers have always been a hot topic in the field of public transport planning [1; 2], because these activities are considered to be inevitable in today's public transportation systems around the world. Urban metro or subway systems, one of highest capacity traffic modes in mass rapid transit (MRT), can provide a reliable, fast, and affordable service for medium-long distance travelers. In contrast, ground-level bus systems are much more flexible to cater passengers who are not served by a metro system, with relatively inexpensive fares and high-level accessibility [3]. Hence, it is cost-effective for commuters to change between the metro and ground-level bus system and it can reduce car usage and traffic congestion in urban areas. However, inefficient transfer design can reduce the attractiveness of public transportation [4]. Nowadays, it is very significant to find and improve poorly connected transfer stations by using the huge transaction data collected by automatic fare collection systems (AFCSs) and automatic vehicle location systems (AVLSs), which can increase the commuters' willingness and mitigate prevalent traffic congestion.  
Before AFCSs and AVLSs, methods for transfer station identification depended on questionnaire data, which is extremely expensive and time-consuming. With the rapid development of information technology and electronic payments, smart cards and global positioning systems (GPS) have been widely applied in public transportation. The former can record passengers' boarding/alighting information when taking metro or ground-level bus vehicles, and the latter can collect bus trajectorieswith a high-frequency sampling rate [5]. Therefore, this study contributes to demonstrating a dynamic station-specific method to automatically recognize transfer trips for each metro station by integrating SC and GPS data. In detail, a novel spatial-temporal rule is implemented to estimate the dynamic elapsed transfer time threshold for each interchange bus station and time period, and to evaluate the level of service for citywide metro-to-bus transfer stations. The objectives of this study are to:  
* Estimate the missing boarding stations for each ground-level bus passenger by integrating bus SC and GPS data, where the system only records the boarding time;
* Develop a new method with station-specific elapsed time threshold estimation to extract actual metro-to-bus interchange trips; and
* Use the dynamic threshold to measure the level of service for metro-to-bus transfer stations, and discover the transfer points with poor connectivity in an urban public transportation network.  
The paper is organized as follows: the next section reviews the relevant literature on metro-bus transfers; the third section provides a detailed description of SC data and GPS data in the city of Shenzhen, China; the fourth section develops a new method of extracting the station-specific elapsed time thresholds based on spatial-temporal rules, and identifies the metro-to-bus interchange trips. In Section 5, we discuss the results of transfer recognition as well as the reliability of the proposed method using a half-monthly dataset; and finally, conclusions and suggestions for future work are presented.