## 3 Data Collection and Analysis
### Data Analysis  
Firstly, this paper developed a rule-based data processing method to filter error data as follows: (1) the tag date for entry and exit time is not on the same day; (2) the recorded transaction time exceeds the operation time of the metro or bus system; (3) invalid data is marked \"NULL\"; (4) passenger holding time in the metro system is longer than 4 h; (5) metro passenger boarding and alighting stations are the same; (6) the interchange time gap from metro to bus is less than 30 s; and (7) the station code cannot be found. Most researchers usually identify the transfer behavior from metro to bus according to the maximum elapsed time between leaving the metro station and arriving at the bus station. The authors assume that the elapsed time threshold of 30 min for some cities recommended by the literatures might be compatible with Shenzhen transit transfer behavior [21; 22; 23]. The distribution of transfer trips can be reached by using the previously collected SC data in the city of Shenzhen as shown in Figures 1 and 2. To evaluate the traditional one-size-fits-all criterion, three kinds of scenarios are analyzed as follows:  
Figure 1: Transfers distribution at different times of day. (**a**) Transfer time distribution. (**b**) Transfer volume distribution. Figure 2: Transfers distribution on festivals and weekends. (**a**) Transfer time distribution. (**b**) Transfer volume distribution. * Scenario 1: different time period on the same day of the weekdays (6:00-23:00 on 11-15 March) on March 2019. * Scenario 2: weekdays (11-15 March, Monday to Friday) vs. weekends (16-17 March, Saturday to Sunday);
* Scenario 3: festivals (February 19 was China Lantern Festival, and March 8 was Women's Day) vs. weekends (17 March was Sunday). Figures 1 and 2 show the average transfer time and the number of transfer trips identified by the previous method with the constant elapsed time threshold of 30 min under three scenarios. Some interesting findings can be reached as follows:  
* The average transfer time and volume per hour are very similar from 7:00 to 21:00 on weekdays. It is consistent with that of [PERSON] et al. [27], who found that the metro-to-bus transfer is relatively stable throughout the week. However, we also find they significantly fluctuate over different time periods on the same day in Figure 1. Particularly, the difference of average transfer time between 6:00 and 17:00 on 14-15 March is up to 4 min. * As shown in Figure 1b, a.m. peak hours are from 7:00-9:00 and p.m. peak hours are from 17:00-19:00, based on estimated transfer volume on weekdays. There is a big difference of transfer behavior between weekdays and weekends in Figure 1b. Especially, the transfer volumes during a.m. and p.m. peak hours on weekdays are much larger than on weekends. * Different from weekends, the transfer passengers waste much more travel time from metro to bus at 14:00 on festival days with a maximal time gap about 2 min greater than on weekends in Figure 2a. Moreover, the transfer volumes between 7:00-16:00 at festivals is much higher than on weekends, and the situation is reversed after 16:00 in Figure 2a. Therefore, it is obvious that the one-size-fits-all criterion is not suitable for each station or all time periods in practice. Meanwhile, it is quite difficult to capture the trend of elapsed time thresholds by using a well-defined mathematic formula due to the large fluctuation of transfer time. Alternatively, this paper proposed a time-varying station-based elapsed time threshold to estimate transfer behavior while considering the following assumptions:  
* The transaction date and time recorded by the smart card system is correct. * The elapsed time threshold of each station should be different because they have different configurations, i.e., the layout of metro station, traffic facilities, pedestrian walkways, and transfer distance, etc.