Geography Reference
In-Depth Information
Table 13.1
Case specific classification models
Duration
(min(h:min))
Duration
(max(h:min))
Fitness
value
Starting time
Purpose
Run
Generations
Age
>
25& weekday
17:26
9:09
14:50
0.8858
Home
42nd
70
7:58
0:50
9:42
0.9025
Work
37th
61
9:13
3:47
7:46
0.6400
School
24th
34
16:02
0:06
3:14
0.5333
Shopping
40th
37
17:53
0:47
2:27
0.6944
Recreation
62nd
60
11:14
0:07
1:58
0.4444
Personal affairs
39th
61
\
\
\
\
Volunteering/
religion
Age
>
25 & weekend
12:46
1:26
20:38
0.9420
Home
40th
33
\
\
\
\
Work
\
\
\
\
School
13:40
0:36
6:24
0.4630
Shopping
38th
67
10:46
1:15
4:02
0.7872
Recreation
27th
35
11:18
0:07
1:56
0.6227
Personal affairs
29th
38
\
\
\
\
Volunteering/
religion
Age
<
D
25 & weekday
11:57
0:36
18:08
0.9112
Home
35th
62
8:06
2:27
10:22
0.8674
Work
29th
42
8:04
1:30
5:08
0.7347
School
21st
52
19:01
0:06
4:33
0.4444
Shopping
36th
57
15:12
0:53
3:27
0.7347
Recreation
31st
26
11:22
0:05
1:49
0.6392
Personal affairs
27th
41
\
\
\
\
Volunteering/
religion
Age
<
D
25 & weekend
11:50
0:11
19:09
0.9481
Home
35th
57
\
\
\
\
Work
\
\
\
\
School
12:21
0:09
4:23
0.7103
Shopping
39th
37
11:04
0:14
12:21
0.8048
Recreation
36th
56
9:42
0:10
2:11
0.6227
Personal affairs
37th
29
9:25
0:17
3:35
0.6400
Volunteering/
religion
24th
9
at places with mixed land use. On the other hand, there are no obvious differences
between the trip purpose categories regarding at which run of the algorithm or at
which generation the best classification models were obtained. This is probably due
to the randomness of the genetic algorithm.
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