Examples of the Implementation of Fuzzy Models in Tourism in the South Moravian Region - Geoinformatics for Intelligent Transportation - page 179

intersect_line_bt intersect buffer_line_points_bt and Points_join_Mam_

aspect_minus

merge_intersects merge of all intersects

btd_net output bike trail sections with assignment of new bike trail

dif culty

The most important step by Python creates btd table for joining to btd_net
feature class with new bike trail dif

field. Initial testing script worked with all
points from feature class merge_intersects and calculated only arithmetic mean of
particular roads according to road id. Because the points are in the different distance
from road line it is more accurate to think about weighted average in the context of
fuzzy membership of points. In the distance 10 m is membership 0 and on the line is
membership 1 (BTD_FUZZY_MEAN).
In this approach we can see the biggest problem in the line direction compared
with the slope direction. Bike trail sections in the direction of the contour line have
a hard dif

culty

culty. And that also brought me to the fact that it will be useful to take
the current dif

'

culty from Mamdani s raster for the line segments in direction slope

' “ ”

line and use dif culty from Mamdani s null raster ( fl (flat surface) for the segments

in direction contour line. Consequently, the dif culty depends on the difference

these rasters and the angle between the slope direction and the road line azimuth.
The product of this deviation of straight lines and the ninetieth of this difference
means the reduction of dif

culty (BTD_FCL_MEAN).
Finally, I tried to reduce mistakes for short road sections and roads in bridges
and at tunnels. The dif ed mainly by

points at their beginning and ending. The points along the line are reduced to the
fl

culty of the bridges and tunnels road is speci

flat value (Fig. 18 ).

8 Conclusion

The bike trail dif culty is important readout for planning routes of bike tours.

Mainly, it depends on the quality of the road surface and the slope. We can express
the requests for the bike trail dif culty fairly verbally by rules that are processed

using the fuzzy sets and the compositional rule of inference and Mamdani ' s

method. This method has reached the best effect with the defuzzi cation the cen-

troid of sums and using the integral calculus.
The main aim of this paper is the exploitation and map presentation of the results
on the web cycling portal of the South Moravian Region http://www.cyklo-jizni-
morava.cz/ . The analysis extends the dif culty of the bike trails to all roads.

Considering fuzzy approach we can imagine the region compactly as a whole of the
seamless bike trail dif

culty
rating. The reclassification of the current difficulty and the update of the road
dif

culty raster fuzzy map and as the map of bike trail dif

culty network are very important to the improvement of the bike routing