Figure 1.
(Subway) Relationship between ridership and coverage.
(Left) We plot the total yearly ridership as a function of
. A linear fit on the
data points gives
(
) which leads to a typical effective length of attraction
per station. (Right) Map of Paris (France) with each subway station represented by a red circle of radius
.
Figure 2.
(Subway) Relation between the length and the number of stations.
(Left) Length of subway networks in the world as a function of the number of stations. A linear fit gives
(Right) Empirical distribution of the inter-station length. The average interstation distance is found to be
and the relative standard deviation is approximately
.
Figure 3.
(Subway) Size of the subway system and city's wealth.
(Left) We plot the number of stations for the different subway systems in the dataset as a function of the Gross Metropolitan Product of the corresponding cities (obtained for subway systems). A linear fit (dashed line) gives
(
). (Subway) Number of lines and number of stations (Right) We plot the number of metro lines
as a function of the number of stations
. A linear fit on the
data points gives
, or, in other words, metro lines comprise on average
stations.
Figure 4.
(Train) Total length and number of stations.
Total length of the national railway network rescaled by the typical size of the country
as a function of the number of stations
. The dashed line shows the best power-law fit on the
data points with an exponent
.
Figure 5.
(Train) Ridership and number of stations.
The total yearly ridership of the railway networks as a function of the number of stations. A linear fit on the
data points gives
(
)
Figure 6.
(Train) Total length of the network and wealth.
Total length of the railway network as a function of the country GDP
. The dashed line shows the linear fit on the
data points which gives
.
Table 1.
Summary of the differences between subways and railways.