-
Notifications
You must be signed in to change notification settings - Fork 0
/
ModelSD.py
313 lines (290 loc) · 16.8 KB
/
ModelSD.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
from smt.sampling_methods import LHS
import pickle
num_exp = 200 ###defined by the user
#Read the data
df = pd.read_csv('Data_1dpb_2dpt_6purp.csv')
INCOME_SCALED = df.FAM_INC_imputed/1000
df['student'] = df.preschool_student+df.primaryschool_student+df.highschool_student
df['student']= df['student'].astype(int)
# BINARIES FOR TOUR NUMBER, PURPOSE, AND COMBINATION INPUTS
# binaries for number of tours in each option
onetour = [0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
twotour = [0,0,0,1,0,1,1,0,0,1,1,0,1,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
threetour = [0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,1,0,1,1,0,0,1,1,0,1,0,0,0,0,0,0,1,0,1,1,0,0,1,1,0,1,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0]
fourtour = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0,0,0,0,1,0,1,1,0,0,1,1,0,1,0,0,0]
#FiveTours = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1,1,0]
#SixTours = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]
# binaries for existance of that specific purpose of tour
WorkT = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
EduT = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
PersonalT = [0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1]
ShopT = [0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1]
LeisureT = [0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1]
EscortT = [0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1]
# binary for tour purpose combination
workedu_tt = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
workpersonal_tt = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1]
workshop_tt = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1]
workleisure_tt = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1]
workescort_tt = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1]
edupersonal_tt = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1]
edushop_tt = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1]
eduleisure_tt = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1]
eduescort_tt = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1]
personalshop_tt = [0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1]
personalleisure_tt = [0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1]
personalescort_tt = [0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,1]
shopleisure_tt = [0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1]
shopescort_tt = [0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,1]
leisureescort_tt = [0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1]
beta_LIC_edu = 0.00766
beta_LIC_escort = 1.13
beta_LIC_personal= 0.225
beta_LIC_rec= 0.224
beta_LIC_shop= 0.114
beta_OnLeave_edu= -1.86
beta_OnLeave_escort = 2.44
beta_OnLeave_leisure= 1.61
beta_OnLeave_personal= 2.05
beta_OnLeave_shop= 1.71
beta_OtherStudent_edu = 7.51
beta_OtherStudent_escort= 1.34
beta_OtherStudent_leisure= 1.27
beta_OtherStudent_personal= 1.32
beta_OtherStudent_shop= 1.67
beta_TRANS_edu = -0.224
beta_TRANS_escort= -0.0304
beta_TRANS_personal = 0.0852
beta_TRANS_rec= 0.0856
beta_TRANS_shop = -0.014
beta_Trainee_edu= 5.65
beta_Trainee_escort= 1.09
beta_Trainee_leisure= 0.257
beta_Trainee_personal= 0.758
beta_Trainee_shop= 0.871
beta_age2025_edu= -0.204
beta_age2025_escort = -1.78
beta_age2025_leisure= 0.13
beta_age2025_personal= -0.146
beta_age2025_shop= -0.189
beta_age2635_edu= -0.278
beta_age2635_escort= -0.55
beta_age2635_leisure= -0.26
beta_age2635_personal= -0.352
beta_age2635_shop = -0.216
beta_age5165_edu= -0.489
beta_age5165_escort= -1.34
beta_age5165_leisure= 0.0155
beta_age5165_personal= 0.119
beta_age5165_shop= 0.043
beta_age65_edu = -0.113
beta_age65_escort= -1.85
beta_age65_personal = 0.37
beta_age65_shop = 0.0558
beta_ageUpto19_edu = -0.00682
beta_ageUpto19_escort = -2.02
beta_ageUpto19_leisure = 0.0389
beta_ageUpto19_personal = -0.231
beta_ageUpto19_shop = -0.832
beta_disabled_edu = -3.41
beta_disabled_escort = 0.832
beta_disabled_leisure = 1.57
beta_disabled_personal = 2.22
beta_disabled_shop = 2.27
beta_eduescort_tt = -0.872
beta_eduleisure_tt = -0.38
beta_edupersonal_tt = -0.91
beta_edushop_tt = -0.976
beta_fam_income_edu = 0.0538
beta_fam_income_escort = 0.0531
beta_fam_income_leisure = 0.0422
beta_fam_income_personal= 0.0229
beta_fam_income_shop = 0.0329
beta_female_edu = 0.0208
beta_female_escort = 0.253
beta_female_leisure = 0.0706
beta_female_personal = -0.0624
beta_female_shop= 0.185
beta_fourtours = -4.52
beta_homemaker_edu = -2.38
beta_homemaker_escort= 1.65
beta_homemaker_leisure= 1.93
beta_homemaker_personal= 1.1
beta_homemaker_shop = 1.92
beta_leisureescort_tt= 0.269
beta_parttime_edu= 1.44
beta_parttime_escort= 0.497
beta_parttime_leisure= 0.452
beta_parttime_personal= 0.764
beta_parttime_shop = 0.548
beta_personalescort_tt = -0.196
beta_personalleisure_tt = 0.0174
beta_personalshop_tt = -0.457
beta_retired_edu = -6.27
beta_retired_escort = 0.964
beta_retired_leisure= 1.85
beta_retired_personal= 1.73
beta_retired_shop = 2.2
beta_shopescort_tt = 0.54
beta_shopleisure_tt = -0.015
beta_student_edu = 8.11
beta_student_escort = 1.33
beta_student_leisure= 1.85
beta_student_personal= 1.25
beta_student_shop = 1.73
beta_threetour = -2.64
beta_tour_edu = -5.35
beta_tour_escort = -3.48
beta_tour_leisure= -1.34
beta_tour_personal= -3
beta_tour_shop = -1.63
beta_twotours = -0.935
beta_unemployed_edu = -2.85
beta_unemployed_escort = 1.48
beta_unemployed_leisure = 1.66
beta_unemployed_personal= 2.24
beta_unemployed_shop = 2.24
beta_universitystudent_edu = 6.47
beta_universitystudent_escort = 1.1
beta_universitystudent_leisure = 0.969
beta_universitystudent_personal = 1.25
beta_universitystudent_shop = 1.54
beta_workescort_tt = 0.705
beta_workleisure_tt = 0.165
beta_workpersonal_tt= -0.913
beta_workshop_tt = -0.298
beta_LIC_travel=0.78
beta_OnLeave_travel=-1.24
beta_OtherStudent_travel=-0.0601
beta_TRANS_travel= 0.391
beta_age2025_travel= -0.274
beta_age2635_travel=0.0252
beta_age5165_travel =-0.232
beta_age65_travel= -0.571
beta_ageUpto19_travel = -0.186
beta_disabled_travel= -1.5
beta_fam_income_travel = 0.000143
beta_female_travel = 0.099
beta_homemaker_travel= -1.73
beta_parttime_travel= -0.155
beta_retired_travel = -0.988
beta_student_travel = 0.61
beta_unemployed_travel = -1.28
beta_universitystudent_travel= -0.436
cons_travel = 1.54
xlimits = np.array([[0.1, 2], [0.1, 2], [0.1, 2]])
sampling = LHS(xlimits=xlimits)
x = sampling(num_exp)
np.random.shuffle(x)
#print(type(x))
x1 = []
x2= []
x3 = []
output = []
for i in range(num_exp):
beta_female_travel = 0.099
beta_TRANS_travel= 0.391
beta_student_travel = 0.61
#Change the beta values
beta_female_travel = beta_female_travel * x[i, 0]
beta_student_travel = beta_student_travel * x[i, 1]
beta_TRANS_travel = beta_TRANS_travel * x[i, 2]
x1.append(beta_female_travel)
x2.append(beta_student_travel)
x3.append(beta_TRANS_travel)
# Utility equations
#V33 is the base: work=1, numberoftours=1 (V33=0)
V= []
for i in range(1,63): #generate utility functions for all activity patterns (although not all will be used int the model)
V.append( beta_tour_edu * (EduT[i]) + beta_tour_personal * (PersonalT[i]) +\
beta_tour_leisure * (LeisureT[i]) + beta_tour_shop * (ShopT[i]) + beta_tour_escort * (EscortT[i])+\
beta_twotours * (twotour[i]) +\
beta_fourtours * (fourtour[i]) +\
beta_workpersonal_tt * (workpersonal_tt[i]) + beta_workleisure_tt * (workleisure_tt[i]) +\
beta_workshop_tt * (workshop_tt[i]) + beta_workescort_tt * (workescort_tt[i]) +\
beta_edupersonal_tt * (edupersonal_tt[i]) + beta_edushop_tt * (edushop_tt[i]) + beta_eduleisure_tt * (eduleisure_tt[i]) +\
beta_eduescort_tt * (eduescort_tt[i]) +\
beta_personalshop_tt * (personalshop_tt[i]) + beta_personalleisure_tt * (personalleisure_tt[i]) + beta_personalescort_tt * (personalescort_tt[i]) +\
beta_shopleisure_tt * (shopleisure_tt[i]) + beta_shopescort_tt * (shopescort_tt[i]) +\
beta_leisureescort_tt * (leisureescort_tt[i]) +\
beta_parttime_edu * (EduT[i] * df.parttime) + beta_parttime_personal * (PersonalT[i] * df.parttime) +\
beta_parttime_leisure * (LeisureT[i] * df.parttime) + beta_parttime_shop * (ShopT[i] * df.parttime) + beta_parttime_escort * (EscortT[i] * df.parttime) +\
beta_retired_edu * (EduT[i] * df.retired) + beta_retired_personal * (PersonalT[i] * df.retired) +\
beta_retired_leisure * (LeisureT[i] * df.retired) + beta_retired_shop * (ShopT[i] * df.retired) + beta_retired_escort * (EscortT[i] * df.retired) +\
beta_disabled_edu * (EduT[i] * df.disabled) + beta_disabled_personal * (PersonalT[i] * df.disabled) +\
beta_disabled_leisure * (LeisureT[i] * df.disabled) + beta_disabled_shop * (ShopT[i] * df.disabled) + beta_disabled_escort * (EscortT[i] * df.disabled) +\
beta_homemaker_edu * (EduT[i] * df.homemaker) + beta_homemaker_personal * (PersonalT[i] * df.homemaker) +\
beta_homemaker_leisure * (LeisureT[i] * df.homemaker) + beta_homemaker_shop * (ShopT[i] * df.homemaker) + beta_homemaker_escort * (EscortT[i] * df.homemaker) +\
beta_OnLeave_edu * (EduT[i] * df.onLeave) + beta_OnLeave_personal * (PersonalT[i] * df.onLeave) +\
beta_OnLeave_leisure * (LeisureT[i] * df.onLeave) + beta_OnLeave_shop * (ShopT[i] * df.onLeave) + beta_OnLeave_escort * (EscortT[i] * df.onLeave) +\
beta_unemployed_edu * (EduT[i] * df.unemployed) + beta_unemployed_personal * (PersonalT[i] * df.unemployed) +\
beta_unemployed_leisure * (LeisureT[i] * df.unemployed) + beta_unemployed_shop * (ShopT[i] * df.unemployed) + beta_unemployed_escort * (EscortT[i] * df.unemployed) +\
beta_universitystudent_edu * (EduT[i] * df.universityStudent) +\
beta_universitystudent_personal * (PersonalT[i] * df.universityStudent) + beta_universitystudent_leisure * (LeisureT[i] * df.universityStudent) +\
beta_universitystudent_shop * (ShopT[i] * df.universityStudent) + beta_universitystudent_escort * (EscortT[i] * df.universityStudent) +\
beta_student_edu * (EduT[i] * df.student) + beta_student_personal * (PersonalT[i] * df.student) +\
beta_student_leisure * (LeisureT[i] * df.student) + beta_student_shop * (ShopT[i] * df.student) + beta_student_escort * (EscortT[i] * df.student) +\
beta_OtherStudent_edu * (EduT[i] * df.other_student) + beta_OtherStudent_personal * (PersonalT[i] * df.other_student) +\
beta_OtherStudent_leisure * (LeisureT[i] * df.other_student) + beta_OtherStudent_shop * (ShopT[i] * df.other_student) + beta_OtherStudent_escort * (EscortT[i] * df.other_student) +\
beta_Trainee_edu * (EduT[i] * df.trainee) + beta_Trainee_personal * (PersonalT[i] * df.trainee) +\
beta_Trainee_leisure * (LeisureT[i] * df.trainee) + beta_Trainee_shop * (ShopT[i] * df.trainee) + beta_Trainee_escort * (EscortT[i] * df.trainee) +\
beta_ageUpto19_edu * (EduT[i] * df.ageUpto19) + beta_ageUpto19_personal * (PersonalT[i] * df.ageUpto19) +\
beta_ageUpto19_leisure * (LeisureT[i] * df.ageUpto19) + beta_ageUpto19_shop * (ShopT[i] * df.ageUpto19) + beta_ageUpto19_escort * (EscortT[i] * df.ageUpto19) +\
beta_age2025_edu * (EduT[i] * df.age2025) + beta_age2025_personal * (PersonalT[i] * df.age2025) +\
beta_age2025_leisure * (LeisureT[i] * df.age2025) + beta_age2025_shop * (ShopT[i] * df.age2025) + beta_age2025_escort * (EscortT[i] * df.age2025) +\
beta_age2635_edu * (EduT[i] * df.age2635) + beta_age2635_personal * (PersonalT[i] * df.age2635) +\
beta_age2635_leisure * (LeisureT[i] * df.age2635) + beta_age2635_shop * (ShopT[i] * df.age2635) + beta_age2635_escort * (EscortT[i] * df.age2635) +\
beta_age5165_edu * (EduT[i] * df.age5165) + beta_age5165_personal * (PersonalT[i] * df.age5165) +\
beta_age5165_leisure * (LeisureT[i] * df.age5165) + beta_age5165_shop * (ShopT[i] * df.age5165) + beta_age5165_escort * (EscortT[i] * df.age5165) +\
beta_age65_edu * (EduT[i] * df.ageMorethan65) + beta_age65_personal * (PersonalT[i] * df.ageMorethan65) +\
beta_age65_shop * (ShopT[i] * df.ageMorethan65) + beta_age65_escort * (EscortT[i] * df.ageMorethan65) +\
beta_female_edu * (EduT[i] * df.female) + beta_female_personal * (PersonalT[i] * df.female) +\
beta_female_leisure * (LeisureT[i] * df.female) + beta_female_shop * (ShopT[i] * df.female) + beta_female_escort * (EscortT[i] * df.female) +\
beta_fam_income_edu * (EduT[i] * INCOME_SCALED) + beta_fam_income_personal * (PersonalT[i] * INCOME_SCALED) +\
beta_fam_income_leisure * (LeisureT[i] * INCOME_SCALED) + beta_fam_income_shop * (ShopT[i] * INCOME_SCALED) + beta_fam_income_escort * (EscortT[i] * INCOME_SCALED) +\
beta_LIC_edu * (EduT[i] * df.DRVLC) + beta_LIC_personal * (PersonalT[i] * df.DRVLC) + beta_LIC_rec * (LeisureT[i] * df.DRVLC) +\
beta_LIC_shop * (ShopT[i] * df.DRVLC) + beta_LIC_escort * (EscortT[i] * df.DRVLC) +\
beta_TRANS_edu * (EduT[i] * df.PTPASS) + beta_TRANS_personal * (PersonalT[i] * df.PTPASS) +\
beta_TRANS_rec * (LeisureT[i] * df.PTPASS) + beta_TRANS_shop * (ShopT[i] * df.PTPASS) + beta_TRANS_escort * (EscortT[i] * df.PTPASS))
V = np.array(V)
Ve= math.e**(V)
dem = sum(Ve)
probs=np.array([math.e**(V[i,:])/dem for i in range(62)])
V1 = np.zeros(len(df))
V2 = cons_travel +\
beta_parttime_travel * df.parttime +\
beta_retired_travel * df.retired +\
beta_disabled_travel * df.disabled +\
beta_homemaker_travel * df.homemaker +\
beta_OnLeave_travel * df.onLeave +\
beta_unemployed_travel * df.unemployed +\
beta_universitystudent_travel * df.universityStudent +\
beta_student_travel * df.student +\
beta_OtherStudent_travel * df.other_student +\
beta_ageUpto19_travel * df.ageUpto19 +\
beta_age2025_travel * df.age2025 +\
beta_age2635_travel * df.age2635 +\
beta_age5165_travel * df.age5165 +\
beta_age65_travel * df.ageMorethan65 +\
beta_female_travel * df.female +\
beta_fam_income_travel * df.FAM_INC_imputed +\
beta_LIC_travel * df.DRVLC +\
beta_TRANS_travel * df.PTPASS
dem= (math.e**(V1)+math.e**(V2))
probs1=np.array([math.e**(V1)/dem,math.e**(V2)/dem])
p_stay= probs1[0].reshape((1, len(df)))
new_probs= probs1[1]*probs
new_probs= np.concatenate((p_stay, new_probs), axis= 0)
choices = [np.random.choice(np.arange(0, 63), p = new_probs[:,i]) for i in range(len(df))]
code= [1 if LeisureT[choices[i]] ==1 else 0 for i in range(len(df))]
output.append(sum(code)) #the return is the number of trips that include leisure
output = np.array(output)
X = x
with open('Data/output200.pickle', 'wb') as handle:
pickle.dump(output, handle, protocol=pickle.HIGHEST_PROTOCOL)
with open('Data/X200.pickle', 'wb') as handle: #labeled ones
pickle.dump(X, handle, protocol=pickle.HIGHEST_PROTOCOL)