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cities with unequal number of points where road traf
c intensities were measured.
The number of points varied from 3 to more than 60. Indicators Q and R were
provided by the Faculty of Economy.
3.3 List of Used Indicators for Correlation
18 types of indicators were used for the correlation. Table 1 shows a complete list
of all indicators (in the
first column) and number of cities where the indicator was
used (second column). They were not complete for all analysed years in all cities.
Correlation coef
cients were calculated from all available values of each indicator
of individual cities.
4 Method
4.1 Correlation Analysis
The analysis of urban development and road traf
c in many cities was performed by
the statistical processing. Correlation analysis was used as a basic tool and corre-
lation coef
cients were evaluated for all cities.
The correlation analysis was evaluated for a correlation coef
cient CCAI
between an average road traf
c intensity and particular statistic indicators. The
average road traf
c intensities
(number of vehicles passing through the location in 24 h) in individual cities
divided by number of locations where the measurement was performed.
The correlation coef
c intensity is a sum of all measured road traf
cient equation
P x x av
ð Þ : P y y a ð Þ
P x x av
CCAI x ; ðÞ ¼
q
ð 1 Þ
: P y y av
2
2
ð
Þ
ð
Þ
was used to
c intensity as
the most important source of pollution in urban areas in most cities of the Czech
Republic.
The correlation coef
find dependence between indicators in Table 1 road traf
cient CCAI as a single value for each city shows how
strong the linear dependency between traf
c intensity and the particular indicator is.
This correlation coef
cient was determined for
three periods: 1970
2005,
-
1970
1990, and 1995
2005 in Microsoft Excel (
corel
function). Values of the
-
-
correlation coef
five classes (Table 2 ):
For a better assessment of each type of a motor vehicles, the
cient were divided into
unit vehicle
(UV)
variable was developed. This variable is de
M,
where T is the number of trucks, O is the number of cars and M is the number of
motorcycles [ 10 ].
ned by: UV ¼
2
T þ O þ
0
5
:
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