## 6 Conclusions  
This study mainly discussed the impact factors of station ridership of Shenzhen metro at a local level during different time periods. Four categories of influencing factors, including land use, socio-economic, network structure, and intermodal transport access, are considered. For data collection of the factors of network structure, what is new compared with prior studies is that we introduce measurements from  
Figure 7: Spatial distribution of local coefficients (elasticities) on weekdays. (**a**) Spatial distribution of local coefficients (elasticities) and t-values (significance) for the population variable. (**b**) Spatial distribution of local coefficients (elasticities) and t-values (significance) for the office variable. the field of network analysis, including degree centrality and betweenness centrality. Compared with the dummy variables mostly applied in the prior studies, these measurements covering comprehensive information can better quantify the network structure factors related to the practical significance of metro networks. Through adopting backward stepwise regression method for variables selection, and [PERSON]'s I spatial autocorrelation test, this paper builds GWR models to analyze the influencing factors of Shenzhen metro ridership at different time resolutions (including the day of week and the time of day) during different time periods (average daily ridership of weekdays, weekends and a whole week, and average hourly ridership of evening rush hours and nonrush hours). Finally, we demonstrate the superiority of GWR models through the case study of Shenzhen metro. Additionally, the impact factors of Shenzhen metro station ridership are explored and analyzed from a local perspective. The experimental results show that GWR models outperform OLS models in terms of both goodness-of-fit and explanatory power. In addition to the coefficient of determination of GWR models dramatically higher than OLS models, GWR models can provide more information about the spatial distribution of models' elasticities and other parameters (e.g., significance and goodness-of-fit). The main findings of this study can be summarized as follows. First, the influencing factors are not entirely consistent as time changes. For different models corresponding to different time periods, the common influencing factors including population, days since stations were opened, and betweenness centrality refer to the major impact factors of Shenzhen metro ridership, while the individual factors can help to address the different impact factors of Shenzhen metro ridership during different time periods. It is found that the main source of the ridership over weekdays is from the commuting, and the ridership is driven by recreational activities related to commerce such as shopping over weekends. In a day, the ridership during evening peak hours is mainly affected by activities from school and commutes, while the ridership at nonrush hours is mainly driven by recreational activities related to commerce. Second, the spatial distribution of elasticities can help us to identify where the specific factor can attract more trips, for example, more trips per capita are expected in the center and mid-north, where commerce, administration, and education are concentrated, while elasticity values are lower in the west and east of Shenzhen. These results of the GWR models show great spatial interpretation in transport planning. Above all, these findings have significant implications for the understanding of transportation planning and periphery development. First, in general, population affects all metro station ridership, especially for the center and mid-north of Shenzhen, indicating that metro station ridership would be significantly increased by an additional resident population in these regions; therefore, the results suggest that it is reasonable to give priority to the planning and construction of the metro lines in the regions of densely distributed resident population.