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SCIENTIA SINICA Informationis, Volume 51 , Issue 9 : 1490(2021) https://doi.org/10.1360/SSI-2020-0303

A novel method to identify influential stations based on dynamic passenger flows

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  • ReceivedOct 3, 2020
  • AcceptedJan 14, 2021
  • PublishedSep 17, 2021

Abstract


Funded by

国家科技部重点研发计划(2019YFB2102304)

国家自然科学基金(61976181,U1803263,11931015)

重庆市自然科学基金(cstc2018jcyjAX0274)

中央高校基本科研业务费专项资金(D5000210738)


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  • Figure 1

    (Color online) (a) The evolution process of NLN. NLN is an abstraction of a subway network, whose structure depends on the topology of the subway network. The load of nodes comes from the statistical analysis of passenger travel data. (b) The schematic diagram of changes in the load of nodes, representing the time-varying changes of passenger flows in stations

  • Figure 2

    (Color online) The evaluation of the critical node based on the TFC criterion. First, based on the network topology in (a), we obtain three values of centrality criteria for nodes in (b). And then the entropy weighting method is used to determine the weight of each centrality criterion in (c). The effect of the network topology on each node is calculated by (2), displayed in (d). Simultaneously, based on the passenger flows of stations in the empirical data as shown in (e), the load of nodes in (f) is initialized, and its influence on each node can be calculated by (3) in (g). Finally, the critical node is determined by (1) combining the network topology and the load of nodes in (h). This example shows that the TFC criterion has the capacity of identifying dynamic changes of the influential station

  • Figure 3

    (Color online) (a) Pearson correlation coefficients for any two days in a week. The horizontal and vertical axes indicate time (i.e., 1 stands for Monday). The color depicts the value of the correlation coefficient. The darker the color, the greater the correlation coefficient. There is a conclusion that any two workdays or two days on the weekend are very similar. However, one workday and one day on the weekend own a dramatic difference. (b) The passenger flows of stations at different time. The results show that most stations will reach the maximum passenger flow at $t_8$ or $t_{18}$. Moreover, the passenger flows differ at the same time in different stations. Therefore, the passenger flows have spatiotemporal characteristics

  • Figure 4

    (Color online) The network average efficiency ($E$) and the relative size of giant component ($S$) corresponding to various importance criteria during different time periods (i.e., $t_8$, $t_{12}$ and $t_{18}$). $p$ represents the proportion of initial failure nodes. The results illustrate that the TFC criterion has a significant downward trend, which can effectively identify the influential station. The reason is that the TFC criterion can examine the station's importance from the perspective of passenger flows, helpful in finding important stations which are difficult to be identified by the network topology. (a) $E$ during $t_8$; (b) $E$ during $t_{12}$; (c) $E$ during $t_{18}$; (d) $S$ during $t_8$; (e) $S$ during $t_{12}$; (f) $S$ during $t_{18}$

  • Figure 5

    (Color online) The comparison of various importance criteria regarding the LPF during (a) $t_{8}$, (b) $t_{12}$ and (c) $t_{18}$. $p$ represents the proportion of initial failure nodes. The figure indicates that the failures are more serious during $t_8$ or $t_{18}$ than $t_{12}$, revealing the dynamic change characteristic of the influential station. Simultaneously, the TFC criterion has achieved better performance than the compared criteria, because it is the combination of the network topology and the load of nodes

  • Figure 6

    (Color online) The comparison results of various importance criteria in terms of (a) the average network efficiency $(E)$, (b) the relative size of giant component $(S)$, and (c) the LPF from the perspective of global time. The importance of stations in the TFC criterion is determined by the average values of the importance of each time period. The results show that the TFC criterion still achieved better performance because it combines the network topology and the passenger flows

  • Figure 7

    (Color online) The Kendall's correlation coefficient ($\tau$) between TFC criterion and $E$ during (a) $t_8$, (b) $t_{12}$ and (c) $t_{18}$, and the $\tau$ between $E$ and (d) SLC, (e) WMI and (f) TRE. A point in the figure represents a station, and $x$-axes and $y$-axes stand for the importance criterion and the network average efficiency, respectively. The results illustrate that the TFC criterion can effectively identify the influential station owing to the consideration of passenger flows in stations, and its correlation with the network average efficiency shows dynamic changes, which concludes that the TFC criterion can reveal the dynamic change characteristic of the influential station

  • Figure 8

    (Color online) The Kendall's correlation coefficient ($\tau$) between LPF and four kinds of importance criteria during time periods (a)$\sim$(d) $t_8$, (e)$\sim$(h) $t_{12}$, and (i)$\sim$(l) $t_{18}$. A point in the figure means a station, and x-axes and y-axes refer to the importance criterion and the LPF, respectively. It can be seen from the figure that the TFC criterion can achieve the best results during all the three time periods. This is because the criterion takes into account the dual effects of the network topology and the load of nodes

  • Figure 9

    (Color online) The evolution of station ranking (rank) with time period (time) is revealed by the intersection of the top 20 stations in each period of the subway network of Shanghai. The results show that the station's importance will change significantly during the periods of violent passenger flow fluctuations (e.g., $t_8$ and $t_{18}$ on workdays). (a) Workdays; (b) weekend

  • Figure 10

    (Color online) (a) The absolute value of the increment of the correlation $|\Delta~{E}|$ between the TFC criterion based on different parameters $(\theta)$ and the average network efficiency $(E)$. (b) The correlation between the LPF based on different parameters $(\beta)$ and various importance criteria

  • Table 1   Data samples stored in the intelligent transportation card of Shanghai subway
    Record User ID Date In_Time In_Station ID Out_Time Out_Station ID
    1 100018830 2015–4–13 8:19 $v_{112}$ 8:52 $v_{123}$
    2 100021809 2015–4–14 17:24 $v_{45}$ 17:57 $v_{36}$
    3 100026571 2015–4–15 13:02 $v_{252}$ 13:56 $v_{240}$
  • Table 2   Kendall's correlation coefficient ($\tau$) between various importance criteria and $S$
    TFC ($t_8$) TFC ($t_{12}$) TFC ($t_{18}$) SLC [35] WMI [7] TRE [25]
    $-0.29$ $-0.30$ $-0.29$ 0.18 $-0.28$ $-0.24$
  • Table 3   The top 5 stations obtained by the TFC criterion based on average passenger flows
    Station Rank TFC DC BC CC
    $v_{13}$ (People's Square) 1 1.780 0.021 0.157 0.105
    $v_{16}$ (Shanghai Railway Station) 2 1.682 0.013 0.239 0.102
    $v_{8}$ (Xujiahui) 3 1.555 0.208 0.186 0.098
    $v_{88}$ (Century Avenue) 4 1.291 0.028 0.232 0.093
    $v_{39}$ (West Nanjing Road) 5 1.287 0.139 0.211 0.106
  • Table 4   The ranking of the top 13 stations obtained by the TFC criterion when using other importance indicators
    Station TFC ($t_8$) SLC [35] WMI [7] TRE [25]
    $v_{13}$ (People's Square) 1 7 5 3
    $v_{16}$ (Shanghai Railway Station) 2 37 2 4
    $v_{8}$ (Xujiahui) 3 1 3 2
    $v_{38}$ (Jing'an Temple) 4 13 17 13
    $v_{88}$ (Century Avenue) 5 17 1 1
    $v_{11}$ (South Shaanxi Road) 6 3 8 6
    $v_{39}$ (West Nanjing Road) 7 2 4 5
    $v_{37}$ (Jiangsu Road) 8 11 7 7
    $v_{40}$ (East Nanjing Road) 9 16 18 14
    $v_{64}$ (Caoyang Road) 10 24 6 8
    $v_{60}$ (Yishan Road) 11 5 13 15
    $v_{65}$ (Zhenping Road) 12 43 11 10
    $v_{41}$ (Lujiazui) 13 72 49 40
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