## 6 Discussion & Conclusions  
The main contribution of this study is the station- and time-specific recognition method for transfer time threshold determination to accurately identify the citywide metro-to-bus interchange trips by using smart card data and GPS data. Firstly, the dynamic and specific elapsed time threshold for each station pair from metro to ground-level bus can be estimated from the metro alighting time at the station and time interval between the metro alighting and the next feeder bus boarding. Thus, metro-to-bus interchange trip recognition is available when the transfer distance and time of a trip meet two spatial-temporal criteria developed by this study. Finally, using transaction data from the metro and bus automatic fare collection system and a person trip survey, we demonstrated that the proposed method can correctly recognize the specific elapsed time thresholds of each station for nine different time periods. The estimated results of transfer time threshold and transfer volume can aid city planners and transit agencies to interpret metro-to-bus interchange behavioral features and evaluate the performance of transit network connectivity.  
In conclusion, one of the advantages of this study is that it presents spatial-temporal dynamic thresholds by comparing with the traditional static methods of one-size-fits-all criterion, which is validated because it has much better goodness-of-fit to surveyed data than the latter. Furthermore, transit agencies are able to diagnose the quality of metro-to-bus connectivity by applying the proposed method to continuous monitoring of transfer time threshold, which will help them to develop policy  
Figure 10: Spatial configuration of metro-to-bus elapsed time threshold during weekday morning peak hours in Shenzhen.  
changes and improve design. For example, the transfer time threshold for 32.06% of the 131 metro stations belonging to Shenzhen Metro Lines 1-5 is more than 30 min during a.m.-peak hours on weekdays, which illustrates that these stations have poor connectivity and require improvement.  
Future research along this line will address the following three issues: (a) validation of the reliability and effectiveness of the station-specific methods under various geometric metro configurations and transfer demand patterns through more extensive field tests in Shenzhen or other cities; (b) transfer behavior analysis and the identification of related impact factors based on demographic data; and (c) improvement of metro stations that are evaluated to have the bad connectivity to ground-level bus stations.  
All authors discussed and commented on the manuscript at all stages. More specifically, [PERSON], [PERSON] and [PERSON] collected the related literatures, conducted the analysis and completed the draft writing under the supervision of [PERSON] and [PERSON]; [PERSON] and [PERSON] contributed to the revision of the paper structure and the presentation style, funding support, as well as the proofreading of the paper.  
This work was supported by National Key R&D Program of China (2018 YFB1601601), Natural Science Foundation of Guangdong Province (2018A030310395), and Science and Technology Program of Guangzhou, China (201803030045). The authors especially appreciate data support from Shenzhen Traffic Operation Command Center, China.  
The authors declare no conflict of interest.