## 5 Case Studies
### Validation with Person Trip Survey Data
#### 5.1.3 Comparisons and Analysis  
In the survey experiments of this study, the volunteers collected a total of 2275 passenger transfer trips from metro to bus. Figures 8 and 9 show the distribution of metro-to-bus passenger transfer time derived from the survey data. Statistically, it is easy to assess whether two independent samples belong to the same distribution via hypothesis testing or not. The Mann-Whitney U test, which is also known as the Wilcoxon rank sum test, is a much better nonparametric test to identify the difference between two groups than the \(t\)-test [35]. Therefore, this study employed the U test to conduct a significance analysis between passenger transfer trips extracted by two methods (the dynamic threshold by this study and static one of 30 min) and on-site survey data. As shown in Table 3, the columns of survey data are the field-measured metro-to-bus statistic transfer data of partial passengers sampled by the eight local volunteers, and the other columns represent the statistic results of all transferred passengers recognized by the dynamic model in this study (Table 2) and the static model with the one-size-fits-all criterion elapsed time threshold of 30 min, respectively. The row data of volume means the total number of transfer trips at the specific time period and metro station, and the average or variance \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Stouoma** &
\begin{tabular}{c} **Shenzhen** \\ **University** \\ \end{tabular} & **Daxin** & **Airport East** \\ \hline
7:00–9:00 on 11–15 March & 25 min & 28 min & 30 min & 19 min \\
18:00–20:00 on 16–17 March & 34 min & 33 min & 28 min & 23 min \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The dynamic elapsed time threshold of four survey metro stations estimated in this study. Figure 6: The cumulative distribution function (CDF) of metro-to-bus transaction time interval at four stations at 7:00–9:00 on 11–15 March. (**a**) Guomao station. (**b**) Shenzhen University station. (**c**) Daxin station. (**d**) Airport East station. Figure 7: The CDF of metro-to-bus transaction time interval at four stations at 18:00–20:00 on 16–17 March. (**a**) Guomao station. (**b**) Shenzhen University station. (**c**) Daxin station. (**d**) Airport East station. represents the statistical value of all trips, respectively. Notably, the surveyed transfer volume is much smaller than the two others because the volunteers could only track a fraction of the transferring passengers in practice. Therefore, our focus is to test the statistical gaps between the three data sources. * From the U test results, the transfer results obtained by the two recognition methods are not significantly different from the survey data at 7:00-9:00 on 21-22 March and 18:00-20:00 on 23-24 March because all \(p\) values are greater than 0.05. In particular, the \(p\) value obtained by the developed dynamic model in this paper is improved by 0.04 and 0.23 compared to the traditional static method at 7:00-9:00 on 21-22 March and 18:00-20:00 on 23-24 March, respectively. These improvements indicate that the station-specific transfer recognition in this study has greater accuracy and reliability to capture actual transfer trips than the one-size-fits-all criterion. * The average transfer trip time of the two recognition methods are a little less than that of the surveyed data. Especially, the estimated average transfer time of four metro stations at 7:00-9:00