## 2 Literature Review  
During the past decades, transfer facilities have been getting more and more focus due to their significance in public transport systems. In the early stage, some researchers investigated the transfer behavior between metro and other transit modes using survey data. For example, [PERSON] et al. analyzed the metro-to-bus transfer in Bangkok based on ordinal regression models, and found that the two greatest factors passengers were concerned about were safety from crime and the transfer distance between metro exits and bus stops [6]. [PERSON] et al. investigated the difference of multiple transfer pairs (metro-to-metro, metro-to-bus, bus-to-metro, and bus-to-bus) via self-reported evaluation of transfer experience and associated factors (walking distance and waiting times) [7]. Depending on the research focus, [PERSON] et al. collected over four thousand transfer trips to metro stations in Beijing and estimated the 75 th-percentile walking distance to metro stations as 494 m in downtown areas and 712 m in suburban areas, respectively [8]. However, these studies suffer from incorrect data recording or false responses. More importantly, it is not feasible to identify and evaluate citywide metro-to-bus transfer behavior in megacities with hundreds of metro stations and thousands of bus stations via surveys. In the late 1990s, smart card payment systems were incorporated into cities, such as Washington D.C. (Smartrip) and Tokyo (Suica). This new technology has rapidly spread to other cities and has become an important component of the modern public transport toll system [9]. [PERSON] et al. revealed that SC data has the potential to profile the characteristics of transit users, such as the number of transfers, boarding time, and hourly trip distribution of different transit modes [10]. A great number of studies has been dedicated to mining SC data for various purposes, such as origin-destination matrices estimation [11], transit user's loyalty measurement [12], travel behavior analysis [13; 14], and transit performance evaluation [15; 16]. [PERSON] et al. analyzed transfer waiting time at major transit hubs [17], where they defined a transfer as a trip that involves two different operators and has a waiting time within 60 min at the same location. [PERSON] et al. also extracted bus-to-bus transfer trips when the two successive boarding times for the same passenger are no greater than 30 min apart by using smart card transaction data [18; 19]. In 2008, [PERSON] et al. investigated multimodal transfer behavior based on SC data from London metro and ground-level bus systems, and recommended transfer elapsed time thresholds for three transfer modes: 20 min for metro-to-bus, 35 min for bus-to-metro, and 45 min for bus-to-bus, respectively [20]. Subsequently, the elapsed time threshold of a 30-min criterion for identifying transfer trips has been widely applied to estimate the daily complete journeys of transit passengers [21; 22; 23]. In particular, [PERSON] et al. developed a method with a transfer time threshold of 30 min to extract high-priority transfer pairs within an SC dataset for Nanjing, China [24]. The developed method failed under the scenarios of long bus time headway or a small number of transfers. Recently, some researchers have also realized that the identification results are not convincing enough when they employed the city-level or route-level uniform static elapsed time threshold to identify the intermodal transfer trips without considering the impact of different stations and time periods. Notably, [PERSON] et al. proposed the concept of maximum interchange time and distance to infer transfer trips [25]. Later, [PERSON] et al. further improved the accuracy of transfer detection by adding off-optimality and total travel time to the criterion [26].