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the speci
c dynamics of network structure, represented in the categorical variable
representing network communities, identi
ed by appropriate technique.
Public transportation network, as described earlier might be decomposed into
nodes, geographically located stops, points of departure and arrival of individual
vehicles on individual lines, and arcs connecting subsequent stops. In the basic
alternative we can use binomial representation for arcs. But network architecture of
usual public transportation systems requires the usage of weighted arcs for cap-
turing changing intensity.
We decide to weight the arcs with integer counts of vehicles, connections
aggregated over an appropriate interval of time. However, such weighting remains
blind to speed of connection. Fast service lines can be signi
cantly preferred to
slow lines. Cheap connections can be preferred to expensive. Comfortable con-
nections can be preferred to crowded lines. For the time being we intentionally
forget about these complications and only consider frequency of connection indi-
rectly also indicating typical waiting time after arriving at the stop randomly.
Further organization of our paper is typical. We
first review an intersection of
literatures discussing basic principles of network research in transportation. We are
especially interested in papers providing theoretical understanding for the evolving
complex network perspective. We also
find similar orientation of medical research in
mapping of the functional brain topologies. Next we describe a representative daily
cycle empirically in the city of Bratislava according to the Apptives schedule as of
July 09, 2013. Network is separated in eight 3-h long samples, which are further
decomposed into Louvain network communities. Basic counts are provided con-
cerning internal and external
ed
between samples. Last section concludes and suggests questions of further interest.
fl
flows relative to full network mobility scale, modi
1.1 Evolving Network Perspective in Literature
Signi
cant progress on the
field of network analysis by Watts and Strogatz [
1
],
Barab
á
si and Albert [
2
] or Albert and Barab
á
si [
3
] have contributed to the
expansion of network analysis in various scienti
fields. Many systems take the
form of networks, sets of nodes or vertices joined together in pairs by links or edges
[
4
]. Examples include social networks [
1
,
5
], technological networks such as the
Internet, the World Wide Web [
6
] and power grids [
7
], and biological networks
such as neural networks [
1
], or metabolic networks [
8
].
During the past few years many studies have focused on different types of
transportation network analyses, especially on airport [
9
,
10
], railway [
11
] or bus
networks [
12
]. Also several public transport systems have been investigated using
various concepts of complex networks [
13
c
16
].
Most of previous studies have analyzed only speci
-
c sub-networks of public
transport networks in various urban areas and in different parts of the world. For
instance, subway network analysis of Boston by Latora and Marchiori [
14
,
17
] who
de
ned measures of local and global network ef
ciencies. They notably found that
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