|
Verhoeven, M., T.A. Arentze, H.J.P. Timmermans and P.J. H.J. van der Waerden (2006), Modeling the influence of structural lifecycle events on activity-travel decisions using a structure learning algorithm. In: Proceedings of the IATBR Conference, Kyoto (CD-ROM, 25 pp.)
Table 1. A classification of events
Cat.
|
Event
|
Examples
|
1
|
Person/family/relatives related
|
Birth, birthday, wedding, anniversary, Mother’s day, Father’s day, etc.
|
2
|
Special days with known date
|
Christmas, St Nicholas, world animal day, old years eve, new years day, carnival, valentines day, queens day, national liberation day, Eastern, Whitsuntide, etc.
|
3
|
Church/school related
|
Baptism, communion, confession, Ramadan, child’s first school day, child’s school excursion, child’s school party, diploma presentation, etc.
|
4
|
Health related
|
Admission or stay in hospital/clinic, therapy, taking a cure, etc.
|
5
|
Maintenance related
|
Car reparation/major maintenance, jobs garden, jobs house, etc.
|
6
|
Sports events
|
Sports day, tournament, football championships on TV, etc.
|
7
|
Social/leisure/recreation events
|
Day out, city trip, shopping, concerts, competitions, games, excursions, parties, fanfares, etc.
|
8
|
Other
|
|
Table 2: Conditional occurrence probabilities of preparation or aftermath activities for an event
|
Person/etc.
|
Special
day
|
Church/
school
|
Health
|
Maintenance
|
Sports
|
Social/
recreation
|
Other
|
Home, social
|
0.012
|
0.016
|
0.000
|
0.000
|
0.007
|
0.000
|
0.001
|
0.000
|
Home, other
|
0.054
|
0.099
|
0.066
|
0.024
|
0.080
|
0.022
|
0.032
|
0.033
|
Shopping
|
0.194
|
0.182
|
0.011
|
0.012
|
0.088
|
0.028
|
0.040
|
0.016
|
Leisure
|
0.007
|
0.003
|
0.022
|
0.006
|
0.000
|
0.017
|
0.006
|
0.000
|
Social
|
0.021
|
0.013
|
0.000
|
0.012
|
0.007
|
0.006
|
0.003
|
0.000
|
Other
|
0.033
|
0.013
|
0.033
|
0.037
|
0.073
|
0.028
|
0.018
|
0.041
|
Total
|
0.320
|
0.326
|
0.132
|
0.091
|
0.255
|
0.101
|
0.101
|
0.090
|
Table 3. Variables used in the model-based analysis
Attribute
|
Label
|
Category
|
Household, composition
|
hComp
|
1: Single, no-worker; 2: Single, worker; 3: Double, one worker; 4: Double, 2 workers, 5: Double, no workers
|
Household, age youngest child
|
hChild
|
0: None; 1: < 4 yr; 2: 5 - 6 yr; 3: 7 - 12 yr; 4: > 12
|
Household, SEC
|
hSEC
|
1: Low; 2: Middle; 3: High
|
Household, Car possession
|
hNcar
|
0: No car; 1: one car; 2: 2 or more cars
|
Person, Position in household
|
Pos
|
1: Head (single or partner); 2: Child; 3: Other
|
Person, Occupancy
|
Occup
|
1: Worker, 2: Student; 3: Other
|
Person, Gender
|
Gend
|
1: Male; 2: Female
|
Person, Age
|
Age
|
1: < 25 yr; 2: 25 – 45 yr; 3: 45 – 64 yr; 4: 65+ yr
|
Person, Driver
|
Driv
|
0: No driving license, 1: Has driving license
|
Person, Education
|
Edu
|
1: Low; 2: Middle; 3: High
|
Person, Work status
|
Work
|
1: No work; 2: Part-time work; 3: Full-time work
|
Event, Type
|
Event
|
See Table 1
|
Event, Transport mode
|
EvMo
|
1: Car driver; 2: Slow; 3: Public transport; 4: Car passenger
|
Event, Travel time (min)
|
EvTt
|
0: No travel; 1: 1-14; 2: 15 - 24; 3: 25-59; 4: 60+
|
Preparation activity, Type
|
PreA
|
0: None, 1: In-home activity, 3: Shopping; 4: Other
|
Preparation activity, Transport Mode
|
PreMo
|
1: Car driver; 2: Slow; 3: Public transport; 4: Car passenger
|
Preparation activity, Travel time
|
PreTt
|
0: No travel; 1: 1-9; 2: 10 - 14; 3: 15-29; 4: 30+;
|
Aftermath activity, Type
|
PostA
|
0: None, 1: In-home activity, 3: Shopping; 4: Other
|
Aftermath activity, Transport Mode
|
PostMo
|
1: Car driver; 2: Slow; 3: Public transport; 4: Car passenger
|
Aftermath activity, Travel time
|
PostTt
|
0: No travel; 1: 1-14; 2: 15 - 44; 3: 45-89; 4: 90+;
|
Table 4. Proportional increase in the a-priori probability given an event, as predicted
Event
|
Shop
|
Ncars 2+
|
Female
|
Driver yes
|
Age 65+
|
No children
|
Mode
car driver
|
Travel long
|
|
(0.09)
|
(0.41)
|
(0.54)
|
(0.87)
|
(0.19)
|
(0.68)
|
(0.44)
|
(0.21)
|
Person/etc.
|
2.21
|
0.97
|
1.03
|
1.03
|
1.10
|
1.02
|
0.88
|
1.01
|
Special day
|
1.94
|
0.84
|
1.02
|
0.86
|
0.83
|
0.87
|
0.64
|
0.84
|
Church/school
|
0.14
|
0.94
|
1.28
|
0.97
|
0.84
|
0.63
|
0.71
|
1.03
|
Health
|
0.15
|
1.04
|
0.98
|
0.96
|
0.81
|
0.89
|
0.95
|
0.65
|
Maintenance
|
1.13
|
0.99
|
0.71
|
1.02
|
0.70
|
0.95
|
0.80
|
0.34
|
Sports
|
0.43
|
0.94
|
0.73
|
0.94
|
1.25
|
0.98
|
0.89
|
0.69
|
Social/recreation
|
0.44
|
1.04
|
1.03
|
1.04
|
1.05
|
1.06
|
1.22
|
1.17
|
Other
|
0.11
|
1.34
|
0.99
|
0.98
|
0.86
|
1.06
|
1.07
|
1.28
|
Figure 2. The network model
Figure 3. The network model after selecting the most relevant socio-economic variables
Figure 4. The network after entering the evidence of a case: illustration
|
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