## 2 Prior Research
### Explanatory and Response Variables  
The explanatory variables used in the models summarized in Table 1 can be categorized into the four main groups of land use, socio-economics, transport accessibility, and network structure variables. Land use variables have been used extensively in previous research. For example, [PERSON] et al. (2015) [21] first evaluated land use characteristics, including the proportions of residential, commercial and office, manufacturing, and mixed land use, within the PCAs of metro stations in the Seoul metropolitan area, and then explored the impact of these characteristics on metro station ridership. [PERSON] et al. (2016) [27] examined the association between land use characteristics at two levels of granularity and public transit ridership in Singapore in time and space. The main socio-economics variables used are population, employment, and automobile ownership ratio. For example, [PERSON] et al. (2004) [5] identified the factors that attracted light-rail ridership in nine U.S. cities. They found that employment within walking distance of each station and residential population were significant factors. [PERSON] et al. (2012) [11] analyzed the determinants of work trip transit ridership in Broward County, Florida. Vehicles per person and parking fees were the potential determinants, and automobile parking fees were found to induce higher levels of transit ridership. As for the third category, transport accessibility, [PERSON] et al. (2010) [9] considered the intermodal competition public transit mode (i.e., the number of bus stations within a station's PCA) as an important factor influencing metro ridership, and they found that the number of bus stations is statistically significant in determining metro ridership in the regression model. [PERSON] et al. (2013) [13] studied how the accessibility of Nanjing metro stations to other modes of transport, such as feeder bus lines stopping at a station and park-and-ride spaces for nonmotor vehicles, influenced metro station ridership, and found that both were significant factors. Finally, in terms of network structure, [PERSON] et al. (2004) [5] considered transfer stations and terminal stations when examining the relationship between transit ridership and the station's properties, using dummy variables to distinguish between these station types. [PERSON] and [PERSON] (2010) [8] and [PERSON] et al. (2012) [11] also considered the factor of transfer station using dummy variables. However, measurements from graph theory and network analysis have not been applied in this research field, although practical significance can be achieved by calculating the degree centrality of nodes to distinguish between transfer, terminal, and normal stations, or by calculating a node's betweenness centrality to indicate how a station allows traffic flows to pass from one part of the metro network to another.  
In terms of the response variable in direct demand models, daily ridership has been the most common concern, as in [PERSON] et al. (2004) [5], [PERSON] (2004) [6], [PERSON] and [PERSON] (2010) [8], [PERSON] et al. (2010) [9], [PERSON] et al. (2013) [13], and [PERSON] and [PERSON] (2014) [26], who all regarded the average weekday ridership as the response variable. [PERSON] et al. (2011) [1] and [PERSON] et al. (2012) [2] considered monthly station ridership as the response in their models. [PERSON] et al. (2003) [24] and [PERSON] et al. (2012) [11] used annual ridership as the response. [PERSON] et al. (2014) [22], [PERSON] et al. (2014) [15], [PERSON] et al. (2016) [27], [PERSON] et.al. (2016) [29], and [PERSON] and [PERSON] (2013) [14] used the shorter hourly analysis interval (e.g., peak hour ridership) as the response. However, few studies have considered the differences in influencing factors between time periods.