## 4 Model Development
### Identification Framework  
In the context of this research, an interchange (or transfer) is defined as a transition between two consecutive journey stages that does not contain a trip-generating activity [25]. In other words,the metro-based journey and the next bus-based one belong to two stages of the same trip, which are strongly correlated at two dimensions of space and time. Thus, it is possible to estimate this transfer behavior based on the combined dataset from smart cards and GPS. To distinguish interchange activities from metro to bus, the threshold of time difference between metro alighting and adjacent bus boarding is normally adopted, such as 30 min [21; 22; 23]. However, the traditional one-size-fits-all criterion is not suitable for each station and all time periods. To do this, the maximum interchange time rule proposed by [PERSON] et al. is applied [25]. This study developed a new method of having a station-specific elapsed time threshold to extract the accurate interchange information. The structure of the identification method is developed in Figure 3.  
**Step 1** Join metro SC data and bus SC data into one table. In Shenzhen, the SC system does not directly record bus boarding station information, so it is necessary to firstly join bus SC data and bus GPS data into a united bus dataset by the descending order of timestamp according to the unique Point of Sale (POS) number recorded by the installed POS terminal. **Step 2** Estimate bus passenger boarding station and time. In the united bus dataset, one can search for the target location of latitude and longitude from the GPS system when the time difference between bus SC and GPS timestamp reaches the minimum and the bus GPS timestamp is ahead of the SC because the passenger swipes his/her card after the bus vehicle completely stops at the station. Then, it is easy to obtain the boarding station when comparing the geographic coordination of each bus station with the latitude and longitude of the estimated target point. **Step 3** Calculate the time difference between the metro alighting time and bus boarding time. Metro SC data and bus boarding information are fused together according to the passenger card number, and one can identify possible transfer trips when the distance between the metro alighting station and bus boarding station for each record is less than the user-defined maximal allowable transfer threshold. **Step 4** Identify the elapsed time threshold for each station and time period based on the spatial-temporal constraints. In the real world, a passenger's transfer time is influenced by many factors, such as transfer distance, bus frequency, arrival time, and bus overload. To address this problem, this study divides nine  
Figure 3: Identification framework based on smart card (SC) data and global positioning system (GPS) data. combinations of time period including three kinds of time of day (a.m.-peak, p.m.-peak, and off-peak) from three kinds of day of week (weekdays, weekends, and festivals). The metro-to-bus elapsed time thresholds for each station and time period will be obtained according to two rules:  
Rule 1: Service area limitation. The service areas of each metro station can be generated according to the maximal allowable nonmotorized distance, such as walking and bike-sharing. Those bus stations within the range of metro station service areas are regarded as candidate interchange targets. Rule 2: Transfer time limitation. Firstly, we record all passengers' travel times from the metro station to a candidate bus station which is less than the upper bound of 40 min. Then, the 95 th percentile of filtered passengers' travel time will be regarded as the metro-to-bus elapsed time threshold for the specific time period. **Step 5** Recognize the validated transfer trips based on the developed station-specific threshold with the usage of the smart card dataset (about 2.78 million metro records of 5 lines and 3.43 million bus records of 985 routes are collected every day by TOCC in Shenzhen).