## 2 Literature Review  
Many previous studies on the functionality of public transport systems were, mainly, based on the passengers' subjective feelings and the services provided by transport facilities. For instance, a study [11] found that the station-area built environment had a significant influence on transport interchanges in the UK and Finland. Another paper [12] studied the travel-mode selection of the Nanjing metro station, by using the hybrid logit model. Their research results showed that the bus-metro-transfer was one of the important travel modes. Research [13] established a large-scale complex hypernetwork, to find out the interconnection mechanism among the bus network, metro network, and bus-metro network. On the other hand, passengers' transfer intention largely depended on their transfer feeling [14]. The study [15] applied structural equation modeling, to explore the impact of perceived values, free bus transfer, and penalties on passengers' bus-metro-transfer intention. They found that the three factors all affect transfer intention between bus and metro. In recent years, metro-smart-card data was commonly used for the research of passenger-travel behavior [16]. Metro-smart-card data have been collected to mine the data chain, such as origin-destination (OD) estimation [16], transit performance evaluation [17; 18], and travel behavior analysis [19; 20]. Based on the survey data from the metro smart card, many appropriate mathematical models have been established, to investigate the transfer behavior between bus and metro. For example, a study [21] developed a fast data-fusion method, based on combined data from bus smart cards and the GPS system, to explore station- and time-specific elapsed-time thresholds, according to the smart card data of Shenzhen, China. Another paper [22] provided a method to measure the transfer quality of bus-metro from two dimensions of time and space, using smart-card data and geographic-information-system (GIS) tools. Research [23] explored the transfer time of different intermodal connections and assessed the transfer service of intermodal connections, based on smart-card data. However, the correlation between the urban built environment and transfer ridership was not fully explored. The reasons may be that in their studies, the built environment was not explicitly described by special variables, such as the bus-metro connection characteristics, land-use attributes, and network attributes of metro stations. The correlation between the built environment and transfer ridership can be investigated by linear and nonlinear models. The linear model may cause wrong analysis results, if built-environment variables have a threshold or nonlinear effect on ridership [24]. To solve this problem, some researchers used log-linear or the Poisson family, to eliminate the error caused by linear analysis [10; 25; 26]. Compared with nonlinear models, linear models addressed the theoretical significance of variables but ignored the practical significance and nonlinear influence of variables [27]. The nonlinear influence of variables was an urgent problem to be solved, in the field of urban-built-environment and travel behavior [28; 29]. In previous studies, many built-environment variables were used for influence analysis on transfer behavior, such as the bus-metro connection characteristics [13], network attributes of metro stations [30], land-use attributes [31; 32], and demographic factors [9]. For a better literature review of the previous related studies, we made a comparison table, shown in Table 1. Bus-metro connection characteristics have a long-term impact on transfer ridership. According to the relevant surveys, the transfer-connection characteristics among transportation modes had a significant impact on travelers' choices, such as the distance, time, and connection characteristics of traffic transfer [22; 30; 33]. A paper [34] concluded that when rail transit did not connect with the bus, the travel preference of rail transit would be greatly reduced.