In [1]:
%load_ext autoreload
%autoreload 2
import orca
from activitysim import defaults
In [2]:
orca.run(["school_location_simulate"])
Running model 'school_location_simulate'
WARNING: Some columns have no variability:
['mode_choice_logsums']
WARNING: Some columns have no variability:
['mode_choice_logsums']
WARNING: Some columns have no variability:
['mode_choice_logsums']
Describe of choices:
count 258.000000
mean 246.577519
std 442.402351
min -1.000000
25% -1.000000
50% -1.000000
75% 352.250000
max 1444.000000
Name: TAZ, dtype: float64
Time to execute model 'school_location_simulate': 9.35s
Total time to execute: 9.35s
In [3]:
orca.run(["workplace_location_simulate"])
Running model 'workplace_location_simulate'
WARNING: Some columns have no variability:
['mode_choice_logsums']
Describe of choices:
count 258.000000
mean 808.961240
std 414.539647
min 1.000000
25% 521.000000
50% 781.000000
75% 1172.750000
max 1452.000000
Name: TAZ, dtype: float64
Time to execute model 'workplace_location_simulate': 7.58s
Total time to execute: 7.58s
In [4]:
print orca.get_table("persons").distance_to_work.describe()
count 258.000000
mean 2.042713
std 1.327850
min 0.220000
25% 1.115000
50% 1.765000
75% 2.710000
max 9.260000
dtype: float64
In [5]:
orca.run(["auto_ownership_simulate"])
Running model 'auto_ownership_simulate'
WARNING: Some columns have no variability:
['@1' 'work_tour_auto_time_savings / (workers+1)']
Choices:
2 38
1 26
3 19
0 12
4 5
dtype: int64
Time to execute model 'auto_ownership_simulate': 0.39s
Total time to execute: 0.39s
In [6]:
orca.run(["cdap_simulate"])
Running model 'cdap_simulate'
Choices:
Home 102
NonMandatory 95
Mandatory 61
dtype: int64
Time to execute model 'cdap_simulate': 6.01s
Total time to execute: 6.01s
In [7]:
orca.run(['mandatory_tour_frequency'])
Running model 'mandatory_tour_frequency'
61 persons run for mandatory tour model
count 61.000000
mean 724.868852
std 432.226271
min 1.000000
25% 409.000000
50% 737.000000
75% 1091.000000
max 1419.000000
Name: workplace_taz, dtype: float64
WARNING: Some columns have no variability:
['(ptype == 6) & student_is_employed' '(ptype == 1) & nonstudent_to_school'
'(ptype == 2) & nonstudent_to_school'
'(ptype == 4) & nonstudent_to_school'
'(ptype == 5) & nonstudent_to_school'
'(ptype == 2) & (auto_ownership == 0)'
'(ptype == 3) & (auto_ownership == 0)'
'(ptype == 5) & (auto_ownership == 0)'
'(ptype == 6) & (auto_ownership == 0)'
'(ptype == 6) & (auto_ownership < drivers)'
'(ptype == 1) * (num_young_children)'
'(ptype == 5) * (num_young_children)'
'(ptype == 6) * (num_young_children)'
'(ptype == 7) * (num_young_children)' '(ptype == 2) & non_family'
'(ptype == 6) & non_family' '(ptype == 6) & home_is_urban'
'(ptype == 7) & home_is_urban' '~(workplace_taz > -1)']
Choices:
work1 32
work2 22
school2 4
school1 3
dtype: int64
Time to execute model 'mandatory_tour_frequency': 4.23s
Total time to execute: 4.23s
In [8]:
orca.get_table("mandatory_tours").tour_type.value_counts()
Out[8]:
work 76
school 11
dtype: int64
In [9]:
orca.run(["mandatory_scheduling"])
Running model 'mandatory_scheduling'
Running 11 school tour scheduling choices
Running 7 #1 tour choices
WARNING: Some columns have no variability:
['(ptype == 1) * start' '(ptype == 1) * duration' '(ptype == 4) * start'
'(workers == hhsize) * duration' '(tour_num > 1) & (start < end_previous)'
'(tour_num > 1) * duration' '(tour_num > 1) & (duration < 6)'
'work_and_school_and_worker & (duration < 6)'
'work_and_school_and_student & (duration < 6)']
Running 4 #2 tour choices
WARNING: Some columns have no variability:
['(ptype == 1) * start' '(ptype == 1) * duration' '(ptype == 4) * start'
'(workers == hhsize) * duration' '(tour_num == 1) * start'
'(tour_num == 1) * duration' '(income_in_thousands >= 100) & (start < 6)'
'(income_in_thousands >= 100) & (end > 22)'
'(tour_num == 1) & (duration < 6)'
'work_and_school_and_worker & (duration < 6)'
'work_and_school_and_student & (duration < 6)']
Running 76 work tour scheduling choices
Running 54 #1 tour choices
WARNING: Some columns have no variability:
['(start <= end_previous) & (tour_num == 2)' '(tour_num == 2) * start'
'(tour_num == 2) * duration' 'home_is_rural & (start < 6)'
'home_is_rural & (end > 22)' '(tour_num == 2) & (duration < 8)'
"(mandatory_tour_frequency == 'work_and_school') & is_worker & (duration < 8)"
"(mandatory_tour_frequency == 'work_and_school') & is_student & (duration < 8)"]
Running 22 #2 tour choices
WARNING: Some columns have no variability:
['(ptype == 3) * start' '(tour_num == 1) * start'
'(tour_num == 1) * duration' 'home_is_rural & (start < 6)'
'home_is_rural & (end > 22)' '(tour_num == 1) & (duration < 8)'
"(mandatory_tour_frequency == 'work_and_school') & is_worker & (duration < 8)"
"(mandatory_tour_frequency == 'work_and_school') & is_student & (duration < 8)"]
Choices:
count 87.000000
mean 151.563218
std 71.138042
min 0.000000
25% 171.500000
50% 188.000000
75% 189.000000
max 189.000000
dtype: float64
Time to execute model 'mandatory_scheduling': 5.17s
Total time to execute: 5.17s
In [10]:
orca.run(['non_mandatory_tour_frequency'])
Running model 'non_mandatory_tour_frequency'
156 persons run for non-mandatory tour model
Running segment 'driving' of size 6
WARNING: Some columns have no variability:
['num_joint_tours*(tot_tours == 0)' 'num_joint_tours*(tot_tours == 1)'
'num_joint_tours*(tot_tours == 2)' 'num_joint_tours*(tot_tours == 3)'
'num_joint_tours*(tot_tours == 4)' 'num_joint_tours*(tot_tours > 4)'
'num_shop_j' 'num_main_j' 'num_eat_j' 'num_visi_j' 'num_disc_j'
'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'
'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'
'max_window*(tot_tours == 4)' 'max_window*(tot_tours > 4)'
'no_cars & (tot_tours == 1)' 'no_cars & (tot_tours == 2)'
'no_cars & (tot_tours == 3)' 'no_cars & (tot_tours == 4)'
'no_cars & (tot_tours > 4)' '(car_sufficiency > 0) & (tot_tours == 1)'
'(car_sufficiency > 0) & (tot_tours == 2)'
'(car_sufficiency > 0) & (tot_tours == 3)'
'(car_sufficiency > 0) & (tot_tours == 4)'
'(car_sufficiency > 0) & (tot_tours > 4)' 'has_driving_kid * escort'
'has_school_kid_at_home * escort' 'has_preschool_kid_at_home * escort'
'has_driving_kid * shopping' 'has_school_kid_at_home * shopping'
'has_preschool_kid_at_home * shopping' 'has_driving_kid * othmaint'
'has_school_kid_at_home * othmaint' 'has_preschool_kid_at_home * othmaint'
'has_driving_kid * eatout' 'has_school_kid_at_home * eatout'
'has_preschool_kid_at_home * eatout' 'has_driving_kid * othdiscr'
'has_school_kid_at_home * othdiscr' 'has_preschool_kid_at_home * othdiscr'
'escort * no_cars']
Running segment 'full' of size 46
WARNING: Some columns have no variability:
['num_joint_tours*(tot_tours == 0)' 'num_joint_tours*(tot_tours == 1)'
'num_joint_tours*(tot_tours == 2)' 'num_joint_tours*(tot_tours == 3)'
'num_joint_tours*(tot_tours == 4)' 'num_joint_tours*(tot_tours > 4)'
'num_shop_j' 'num_main_j' 'num_eat_j' 'num_visi_j' 'num_disc_j'
'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'
'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'
'max_window*(tot_tours == 4)' 'max_window*(tot_tours > 4)'
'(car_sufficiency > 0) & (tot_tours == 1)'
'(car_sufficiency > 0) & (tot_tours == 2)'
'(car_sufficiency > 0) & (tot_tours == 3)'
'(car_sufficiency > 0) & (tot_tours == 4)'
'(car_sufficiency > 0) & (tot_tours > 4)'
'has_school_kid_at_home * escort' 'has_preschool_kid_at_home * escort'
'has_school_kid_at_home * shopping' 'has_preschool_kid_at_home * shopping'
'has_school_kid_at_home * othmaint' 'has_preschool_kid_at_home * othmaint'
'has_school_kid_at_home * eatout' 'has_preschool_kid_at_home * eatout'
'has_school_kid_at_home * othdiscr' 'has_preschool_kid_at_home * othdiscr']
Running segment 'nonwork' of size 38
WARNING: Some columns have no variability:
['num_joint_tours*(tot_tours == 0)' 'num_joint_tours*(tot_tours == 1)'
'num_joint_tours*(tot_tours == 2)' 'num_joint_tours*(tot_tours == 3)'
'num_joint_tours*(tot_tours == 4)' 'num_joint_tours*(tot_tours > 4)'
'num_shop_j' 'num_main_j' 'num_eat_j' 'num_visi_j' 'num_disc_j'
'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'
'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'
'max_window*(tot_tours == 4)' 'max_window*(tot_tours > 4)'
'has_retiree & (tot_tours == 1)' 'has_retiree & (tot_tours == 2)'
'has_retiree & (tot_tours == 3)' 'has_retiree & (tot_tours == 4)'
'has_retiree & (tot_tours == 5)' 'has_retiree * escort'
'has_school_kid_at_home * escort' 'has_preschool_kid_at_home * escort'
'has_retiree * shopping' 'has_school_kid_at_home * shopping'
'has_preschool_kid_at_home * shopping' 'has_retiree * othmaint'
'has_school_kid_at_home * othmaint' 'has_preschool_kid_at_home * othmaint'
'has_retiree * eatout' 'has_school_kid_at_home * eatout'
'has_preschool_kid_at_home * eatout' 'has_retiree * othdiscr'
'has_school_kid_at_home * othdiscr' 'has_preschool_kid_at_home * othdiscr']
Running segment 'part' of size 13
WARNING: Some columns have no variability:
['num_joint_tours*(tot_tours == 0)' 'num_joint_tours*(tot_tours == 1)'
'num_joint_tours*(tot_tours == 2)' 'num_joint_tours*(tot_tours == 3)'
'num_joint_tours*(tot_tours == 4)' 'num_joint_tours*(tot_tours > 4)'
'num_shop_j' 'num_main_j' 'num_eat_j' 'num_visi_j' 'num_disc_j'
'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'
'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'
'max_window*(tot_tours == 4)' 'max_window*(tot_tours > 4)'
'no_cars & (tot_tours == 1)' 'no_cars & (tot_tours == 2)'
'no_cars & (tot_tours == 3)' 'no_cars & (tot_tours == 4)'
'no_cars & (tot_tours > 4)' '(car_sufficiency > 0) & (tot_tours == 1)'
'(car_sufficiency > 0) & (tot_tours == 2)'
'(car_sufficiency > 0) & (tot_tours == 3)'
'(car_sufficiency > 0) & (tot_tours == 4)'
'(car_sufficiency > 0) & (tot_tours > 4)'
'has_school_kid_at_home * escort' 'has_preschool_kid_at_home * escort'
'has_school_kid_at_home * shopping' 'has_preschool_kid_at_home * shopping'
'has_school_kid_at_home * othmaint' 'has_preschool_kid_at_home * othmaint'
'has_school_kid_at_home * eatout' 'has_preschool_kid_at_home * eatout'
'has_school_kid_at_home * othdiscr' 'has_preschool_kid_at_home * othdiscr'
'escort * no_cars']
Running segment 'preschool' of size 12
WARNING: Some columns have no variability:
['num_joint_tours*(tot_tours == 0)' 'num_joint_tours*(tot_tours == 1)'
'num_joint_tours*(tot_tours == 2)' 'num_joint_tours*(tot_tours == 3)'
'num_joint_tours*(tot_tours == 4)' 'num_joint_tours*(tot_tours > 4)'
'num_shop_j' 'num_main_j' 'num_eat_j' 'num_visi_j' 'num_disc_j'
'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'
'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'
'max_window*(tot_tours == 4)' 'max_window*(tot_tours > 4)'
'(100 < income_in_thousands) & (tot_tours == 1)'
'(100 < income_in_thousands) & (tot_tours == 2)'
'(100 < income_in_thousands) & (tot_tours == 3)'
'(100 < income_in_thousands) & (tot_tours == 4)'
'(100 < income_in_thousands) & (tot_tours > 4)'
'(100 < income_in_thousands) * shopping'
'(100 < income_in_thousands) * othmaint'
'(100 < income_in_thousands) * eatout'
'(100 < income_in_thousands) * othdiscr'
'(100 < income_in_thousands) * social' 'no_cars & (tot_tours == 1)'
'no_cars & (tot_tours == 2)' 'no_cars & (tot_tours == 3)'
'no_cars & (tot_tours == 4)' 'no_cars & (tot_tours > 4)'
'has_school_kid_at_home * escort' 'has_preschool_kid_at_home * escort'
'has_school_kid_at_home * shopping' 'has_preschool_kid_at_home * shopping'
'has_school_kid_at_home * othmaint' 'has_preschool_kid_at_home * othmaint'
'has_school_kid_at_home * eatout' 'has_preschool_kid_at_home * eatout'
'has_school_kid_at_home * othdiscr' 'has_preschool_kid_at_home * othdiscr'
'escort * no_cars']
Running segment 'retired' of size 14
WARNING: Some columns have no variability:
['num_joint_tours*(tot_tours == 0)' 'num_joint_tours*(tot_tours == 1)'
'num_joint_tours*(tot_tours == 2)' 'num_joint_tours*(tot_tours == 3)'
'num_joint_tours*(tot_tours == 4)' 'num_joint_tours*(tot_tours > 4)'
'num_shop_j' 'num_main_j' 'num_eat_j' 'num_visi_j' 'num_disc_j'
'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'
'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'
'max_window*(tot_tours == 4)' 'max_window*(tot_tours > 4)'
'(100 < income_in_thousands) & (tot_tours == 1)'
'(100 < income_in_thousands) & (tot_tours == 2)'
'(100 < income_in_thousands) & (tot_tours == 3)'
'(100 < income_in_thousands) & (tot_tours == 4)'
'(100 < income_in_thousands) & (tot_tours > 4)'
'(100 < income_in_thousands) * shopping'
'(100 < income_in_thousands) * othmaint'
'(100 < income_in_thousands) * eatout'
'(100 < income_in_thousands) * othdiscr'
'(100 < income_in_thousands) * social'
'(car_sufficiency > 0) & (tot_tours == 1)'
'(car_sufficiency > 0) & (tot_tours == 2)'
'(car_sufficiency > 0) & (tot_tours == 3)'
'(car_sufficiency > 0) & (tot_tours == 4)'
'(car_sufficiency > 0) & (tot_tours > 4)'
'has_non_worker & (tot_tours == 1)' 'has_non_worker & (tot_tours == 2)'
'has_non_worker & (tot_tours == 3)' 'has_non_worker & (tot_tours == 4)'
'has_non_worker & (tot_tours == 5)' 'has_preschool_kid & (tot_tours == 1)'
'has_preschool_kid & (tot_tours == 2)'
'has_preschool_kid & (tot_tours == 3)'
'has_preschool_kid & (tot_tours == 4)'
'has_preschool_kid & (tot_tours == 5)' 'has_non_worker * escort'
'has_driving_kid * escort' 'has_preschool_kid * escort'
'has_school_kid_at_home * escort' 'has_preschool_kid_at_home * escort'
'has_non_worker * shopping' 'has_driving_kid * shopping'
'has_preschool_kid * shopping' 'has_school_kid_at_home * shopping'
'has_preschool_kid_at_home * shopping' 'has_non_worker * othmaint'
'has_driving_kid * othmaint' 'has_preschool_kid * othmaint'
'has_school_kid_at_home * othmaint' 'has_preschool_kid_at_home * othmaint'
'has_non_worker * eatout' 'has_driving_kid * eatout'
'has_preschool_kid * eatout' 'has_school_kid_at_home * eatout'
'has_preschool_kid_at_home * eatout' 'has_non_worker * othdiscr'
'has_driving_kid * othdiscr' 'has_preschool_kid * othdiscr'
'has_school_kid_at_home * othdiscr' 'has_preschool_kid_at_home * othdiscr']
Running segment 'school' of size 19
WARNING: Some columns have no variability:
['num_joint_tours*(tot_tours == 0)' 'num_joint_tours*(tot_tours == 1)'
'num_joint_tours*(tot_tours == 2)' 'num_joint_tours*(tot_tours == 3)'
'num_joint_tours*(tot_tours == 4)' 'num_joint_tours*(tot_tours > 4)'
'num_shop_j' 'num_main_j' 'num_eat_j' 'num_visi_j' 'num_disc_j'
'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'
'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'
'max_window*(tot_tours == 4)' 'max_window*(tot_tours > 4)'
'has_university * escort' 'has_school_kid_at_home * escort'
'has_preschool_kid_at_home * escort' 'has_university * shopping'
'has_school_kid_at_home * shopping' 'has_preschool_kid_at_home * shopping'
'has_university * othmaint' 'has_school_kid_at_home * othmaint'
'has_preschool_kid_at_home * othmaint' 'has_university * eatout'
'has_school_kid_at_home * eatout' 'has_preschool_kid_at_home * eatout'
'has_university * othdiscr' 'has_school_kid_at_home * othdiscr'
'has_preschool_kid_at_home * othdiscr']
Running segment 'university' of size 8
WARNING: Some columns have no variability:
['num_joint_tours*(tot_tours == 0)' 'num_joint_tours*(tot_tours == 1)'
'num_joint_tours*(tot_tours == 2)' 'num_joint_tours*(tot_tours == 3)'
'num_joint_tours*(tot_tours == 4)' 'num_joint_tours*(tot_tours > 4)'
'num_shop_j' 'num_main_j' 'num_eat_j' 'num_visi_j' 'num_disc_j'
'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'
'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'
'max_window*(tot_tours == 4)' 'max_window*(tot_tours > 4)'
'no_cars & (tot_tours == 1)' 'no_cars & (tot_tours == 2)'
'no_cars & (tot_tours == 3)' 'no_cars & (tot_tours == 4)'
'no_cars & (tot_tours > 4)' 'has_retiree & (tot_tours == 1)'
'has_retiree & (tot_tours == 2)' 'has_retiree & (tot_tours == 3)'
'has_retiree & (tot_tours == 4)' 'has_retiree & (tot_tours == 5)'
'has_school_kid & (tot_tours == 1)' 'has_school_kid & (tot_tours == 2)'
'has_school_kid & (tot_tours == 3)' 'has_school_kid & (tot_tours == 4)'
'has_school_kid & (tot_tours == 5)' 'has_retiree * escort'
'has_school_kid * escort' 'has_school_kid_at_home * escort'
'has_preschool_kid_at_home * escort' 'has_retiree * shopping'
'has_school_kid * shopping' 'has_school_kid_at_home * shopping'
'has_preschool_kid_at_home * shopping' 'has_retiree * othmaint'
'has_school_kid * othmaint' 'has_school_kid_at_home * othmaint'
'has_preschool_kid_at_home * othmaint' 'has_retiree * eatout'
'has_school_kid * eatout' 'has_school_kid_at_home * eatout'
'has_preschool_kid_at_home * eatout' 'has_retiree * othdiscr'
'has_school_kid * othdiscr' 'has_school_kid_at_home * othdiscr'
'has_preschool_kid_at_home * othdiscr' 'escort * no_cars']
Choices:
0 61
1 21
16 17
8 9
4 8
2 7
64 6
32 5
17 3
3 2
80 2
33 2
20 1
72 1
21 1
24 1
13 1
12 1
9 1
25 1
5 1
48 1
53 1
56 1
18 1
dtype: int64
Time to execute model 'non_mandatory_tour_frequency': 11.53s
Total time to execute: 11.53s
In [11]:
orca.get_table("non_mandatory_tours").tour_type.value_counts()
Out[11]:
othdiscr 34
shopping 30
escort 28
othmaint 16
eatout 14
social 10
dtype: int64
In [12]:
orca.run(["destination_choice"])
Running model 'destination_choice'
Running segment 'eatout' of size 14
Running segment 'shopping' of size 28
Running segment 'othdiscr' of size 34
Running segment 'othmaint' of size 16
Running segment 'shopping' of size 30
Running segment 'social' of size 10
Choices:
count 132.000000
mean 807.893939
std 402.766374
min 1.000000
25% 559.000000
50% 746.500000
75% 1134.250000
max 1452.000000
Name: TAZ, dtype: float64
Time to execute model 'destination_choice': 5.24s
Total time to execute: 5.24s
In [13]:
orca.run(["non_mandatory_scheduling"])
Running model 'non_mandatory_scheduling'
Running 132 non-mandatory tour scheduling choices
Running 95 #1 tour choices
WARNING: Some columns have no variability:
['(start <= end_previous) & (tour_num > 1)'
'(tour_type == "oth_maint") * start'
'(tour_type == "oth_maint") * duration' 'num_mand * start'
'num_joint_tours * start' '(tour_num > 1) * duration'
'(tour_type == "oth_maint") & (start < 7)'
'(tour_type == "othdisc") & (duration < 2)']
Running 28 #2 tour choices
WARNING: Some columns have no variability:
['(tour_type == "oth_maint") * start'
'(tour_type == "oth_maint") * duration' '(ptype == 6) * start'
'(ptype == 6) * duration' 'num_mand * start' 'num_joint_tours * start'
'(tour_num == 1) * start' '(tour_type == "oth_maint") & (start < 7)'
'(tour_type == "othdisc") & (duration < 2)']
Running 8 #3 tour choices
WARNING: Some columns have no variability:
['(tour_type == "oth_maint") * start'
'(tour_type == "oth_maint") * duration' '(tour_type == "social") * start'
'(tour_type == "social") * duration' '(ptype == 6) * start'
'(ptype == 6) * duration' '(ptype == 7) * start' '(ptype == 7) * duration'
'destination_in_cbd * duration' 'num_mand * start'
'num_joint_tours * start' '(tour_num == 1) * start'
'(tour_type == "oth_maint") & (start < 7)' '(ptype == 7) & (end > 22)'
'(tour_type == "othdisc") & (duration < 2)'
'(tour_type == "escort") & (start < 6)'
'(tour_type == "escort") & (start == 6)'
'(tour_type == "escort") & (start == 7)'
'(tour_type == "escort") & (start == 8)'
'(tour_type == "escort") & (start == 9)'
'(tour_type == "escort") & (start > 9) & (start < 13)'
'(tour_type == "escort") & (start > 12) & (start < 16)'
'(tour_type == "escort") & (start > 15) & (start < 19)'
'(tour_type == "escort") & (start > 18) & (start < 22)'
'(tour_type == "escort") & (start > 21)'
'(tour_type == "escort") & (end < 7)'
'(tour_type == "escort") & (end > 6) & (end < 10)'
'(tour_type == "escort") & (end > 9) & (end < 13)'
'(tour_type == "escort") & (end > 12) & (end < 15)'
'(tour_type == "escort") & (end == 15)'
'(tour_type == "escort") & (end == 16)'
'(tour_type == "escort") & (end == 17)'
'(tour_type == "escort") & (end == 18)'
'(tour_type == "escort") & (end > 18) & (end < 22)'
'(tour_type == "escort") & (end > 21)'
'(tour_type == "escort") & (duration < 2)'
'(tour_type == "escort") & (duration > 1) & (duration < 4)'
'(tour_type == "escort") & (duration > 3) & (duration < 6)'
'(tour_type == "escort") & (duration > 5) & (duration < 8)'
'(tour_type == "escort") & (duration > 7) & (duration < 11)'
'(tour_type == "escort") & (duration > 10) & (duration < 14)'
'(tour_type == "escort") & (duration > 13) & (duration < 19)']
Running 1 #4 tour choices
WARNING: Some columns have no variability:
['(start <= end_previous) & (tour_num > 1)'
'(tour_type == "shopping") * start' '(tour_type == "shopping") * duration'
'(tour_type == "oth_maint") * start'
'(tour_type == "oth_maint") * duration' '(tour_type == "social") * start'
'(tour_type == "social") * duration' '(ptype == 6) * start'
'(ptype == 6) * duration' '(ptype == 7) * start' '(ptype == 7) * duration'
'destination_in_cbd * duration' 'num_mand * start'
'num_joint_tours * start' '(tour_num == 1) * start'
'(tour_type == "oth_maint") & (start < 7)'
'(tour_type == "shopping") & (start < 8)'
'(tour_type == "shopping") & (end > 22)' '(ptype == 7) & (end > 22)'
'(ptype == 3) & (end > 22)' '(tour_type == "shopping") & (duration < 2)'
'(tour_type == "othdisc") & (duration < 2)'
'adult & (num_children > 0) & ( end > 18 ) & ( end < 22 )'
'(tour_type == "escort") & (start < 6)'
'(tour_type == "escort") & (start == 6)'
'(tour_type == "escort") & (start == 7)'
'(tour_type == "escort") & (start == 8)'
'(tour_type == "escort") & (start == 9)'
'(tour_type == "escort") & (start > 9) & (start < 13)'
'(tour_type == "escort") & (start > 12) & (start < 16)'
'(tour_type == "escort") & (start > 15) & (start < 19)'
'(tour_type == "escort") & (start > 18) & (start < 22)'
'(tour_type == "escort") & (start > 21)'
'(tour_type == "escort") & (end < 7)'
'(tour_type == "escort") & (end > 6) & (end < 10)'
'(tour_type == "escort") & (end > 9) & (end < 13)'
'(tour_type == "escort") & (end > 12) & (end < 15)'
'(tour_type == "escort") & (end == 15)'
'(tour_type == "escort") & (end == 16)'
'(tour_type == "escort") & (end == 17)'
'(tour_type == "escort") & (end == 18)'
'(tour_type == "escort") & (end > 18) & (end < 22)'
'(tour_type == "escort") & (end > 21)'
'(tour_type == "escort") & (duration < 2)'
'(tour_type == "escort") & (duration > 1) & (duration < 4)'
'(tour_type == "escort") & (duration > 3) & (duration < 6)'
'(tour_type == "escort") & (duration > 5) & (duration < 8)'
'(tour_type == "escort") & (duration > 7) & (duration < 11)'
'(tour_type == "escort") & (duration > 10) & (duration < 14)'
'(tour_type == "escort") & (duration > 13) & (duration < 19)']
Choices:
count 132.000000
mean 150.969697
std 75.061597
min 0.000000
25% 187.000000
50% 188.000000
75% 189.000000
max 189.000000
dtype: float64
Time to execute model 'non_mandatory_scheduling': 6.12s
Total time to execute: 6.12s
In [14]:
orca.run(['mode_choice_simulate'])
Running model 'mode_choice_simulate'
Expression Rowid Alternative
sov_available == False 0 DRIVEALONEFREE -999.000000
auto_ownership == 0 1 DRIVEALONEFREE -999.000000
age < 16 2 DRIVEALONEFREE -999.000000
is_joint 3 DRIVEALONEFREE -999.000000
work_tour_is_drive 4 DRIVEALONEFREE -999.000000
@out_skims['SOV_TIME'] + in_skims['SOV_TIME'] 5 DRIVEALONEFREE -0.017500
2 * terminal_time 6 DRIVEALONEFREE -0.035000
@costPerMile * (out_skims['SOV_DIST'] + in_skims['SOV_DIST']) 7 DRIVEALONEFREE -0.001313
daily_parking_cost 8 DRIVEALONEFREE -0.001313
@out_skims['SOV_BTOLL'] + in_skims['SOV_BTOLL'] 9 DRIVEALONEFREE -0.001313
age_16_to_19 10 DRIVEALONEFREE 0.000000
sovtoll_available == False 11 DRIVEALONEPAY -999.000000
auto_ownership == 0 12 DRIVEALONEPAY -999.000000
age < 16 13 DRIVEALONEPAY -999.000000
is_joint 14 DRIVEALONEPAY -999.000000
work_tour_is_drive 15 DRIVEALONEPAY -999.000000
@out_skims['SOVTOLL_TIME'] + in_skims['SOVTOLL_TIME'] 16 DRIVEALONEPAY -0.017500
2 * terminal_time 17 DRIVEALONEPAY -0.035000
@costPerMile * (out_skims['SOVTOLL_DIST'] + in_skims['SOVTOLL_DIST']) 18 DRIVEALONEPAY -0.001313
daily_parking_cost 19 DRIVEALONEPAY -0.001313
@out_skims['SOVTOLL_BTOLL'] + in_skims['SOVTOLL_BTOLL'] 20 DRIVEALONEPAY -0.001313
@out_skims['SOVTOLL_VTOLL'] + in_skims['SOVTOLL_VTOLL'] 21 DRIVEALONEPAY -0.001313
age_16_to_19 22 DRIVEALONEPAY 0.000000
hov2_available == False 23 SHARED2FREE -999.000000
is_joint & (number_of_participants > 2) 24 SHARED2FREE -999.000000
@out_skims['HOV2_TIME'] + in_skims['HOV2_TIME'] 25 SHARED2FREE -0.017500
2 * terminal_time 26 SHARED2FREE -0.035000
@costPerMile * (out_skims['HOV2_DIST'] + in_skims['HOV2_DIST']) 27 SHARED2FREE -0.001313
@df.daily_parking_cost / costShareSr2 28 SHARED2FREE -0.001313
@(out_skims['HOV2_BTOLL'] + in_skims['HOV2_BTOLL']) / costShareSr2 29 SHARED2FREE -0.001313
...
1 257 DRIVE_EXP 0.525000
DRIVE_HVY 0.525000
DRIVE_COM 0.525000
258 DRIVE_LOC 0.525000
DRIVE_LRF 0.525000
DRIVE_EXP 0.525000
DRIVE_HVY 0.525000
DRIVE_COM 0.525000
259 WALK_LOC -0.090703
DRIVE_LOC -0.090703
260 WALK_LRF 0.940124
261 DRIVE_LRF 0.940124
262 WALK_LRF 0.940124
263 DRIVE_LRF 0.940124
264 WALK_EXP 0.969232
DRIVE_EXP 0.969232
265 WALK_HVY 0.770612
DRIVE_HVY 0.770612
266 WALK_COM 0.727019
DRIVE_COM 0.727019
267 WALK_LOC 0.525000
WALK_LRF 0.525000
WALK_EXP 0.525000
WALK_HVY 0.525000
WALK_COM 0.525000
268 DRIVE_LOC 0.525000
DRIVE_LRF 0.525000
DRIVE_EXP 0.525000
DRIVE_HVY 0.525000
DRIVE_COM 0.525000
Name: eatout, dtype: float64
WARNING, skipping key: DISTANCE
WARNING, skipping key: DISTANCE
WARNING: Some columns have no variability:
['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' '1' '1' '1' '1'
'1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '2 * destination_walk_time'
'2 * destination_walk_time' '2 * destination_walk_time'
'2 * destination_walk_time' '2 * destination_walk_time'
'2 * destination_walk_time' '2 * destination_walk_time'
'2 * destination_walk_time' '2 * destination_walk_time'
'2 * destination_walk_time' '2 * origin_walk_time' '2 * origin_walk_time'
'2 * origin_walk_time' '2 * origin_walk_time' '2 * origin_walk_time'
'2 * origin_walk_time' '2 * origin_walk_time' '2 * origin_walk_time'
'2 * origin_walk_time' '2 * origin_walk_time' '2 * terminal_time'
'2 * terminal_time' '2 * terminal_time' '2 * terminal_time'
'2 * terminal_time' '2 * terminal_time'
"@(out_skims['DRV_COM_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_COM_DRV_IWAIT']/100-waitThresh).clip(0)"
"@(out_skims['DRV_EXP_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_EXP_DRV_IWAIT']/100-waitThresh).clip(0)"
"@(out_skims['DRV_HVY_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_HVY_DRV_IWAIT']/100-waitThresh).clip(0)"
"@(out_skims['DRV_LOC_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_LOC_DRV_IWAIT']/100-waitThresh).clip(0)"
"@(out_skims['DRV_LRF_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_LRF_DRV_IWAIT']/100-waitThresh).clip(0)"
"@(out_skims['WLK_COM_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_COM_WLK_IWAIT']/100-waitThresh).clip(0)"
"@(out_skims['WLK_EXP_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_EXP_WLK_IWAIT']/100-waitThresh).clip(0)"
"@(out_skims['WLK_HVY_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_HVY_WLK_IWAIT']/100-waitThresh).clip(0)"
"@(out_skims['WLK_LOC_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_LOC_WLK_IWAIT']/100-waitThresh).clip(0)"
"@(out_skims['WLK_LRF_WLK_IWAIT']/100-waitThresh).clip(0)+ (in_skims['WLK_LRF_WLK_IWAIT']/100-waitThresh).clip(0)"
'@df.daily_parking_cost / costShareSr2'
'@df.daily_parking_cost / costShareSr2'
'@df.daily_parking_cost / costShareSr3'
'@df.daily_parking_cost / costShareSr3' 'auto_ownership == 0'
'auto_ownership == 0' 'auto_ownership == 0' 'auto_ownership == 0'
'auto_ownership == 0' 'auto_ownership == 0' 'auto_ownership == 0'
'daily_parking_cost' 'daily_parking_cost'
'drive_commuter_available == False' 'drive_express_available == False'
'drive_heavyrail_available == False' 'drive_local_available == False'
'drive_lrf_available == False' 'hhsize == 1' 'hhsize == 1' 'hhsize == 1'
'hhsize == 1' 'hov2_available == False' 'hov2toll_available == False'
'hov3_available == False' 'hov3_available == False' 'is_joint' 'is_joint'
'is_joint & (number_of_participants > 2)'
'is_joint & (number_of_participants > 2)' 'sov_available == False'
'sovtoll_available == False' 'walk_commuter_available == False'
'walk_express_available == False' 'walk_heavyrail_available == False'
'walk_local_available == False' 'walk_lrf_available == False'
'work_tour_is_drive' 'work_tour_is_drive']
Choices:
WALK_LRF 3
WALK_EXP 3
DRIVE_LOC 2
DRIVE_HVY 2
WALK_LOC 1
WALK_COM 1
DRIVE_LRF 1
DRIVE_EXP 1
dtype: int64
Time to execute model 'mode_choice_simulate': 6.96s
Total time to execute: 6.96s
In [17]:
orca.get_table("land_use").to_frame().info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1454 entries, 1 to 1454
Data columns (total 49 columns):
DISTRICT 1454 non-null int64
SD 1454 non-null int64
COUNTY 1454 non-null int64
TOTHH 1454 non-null int64
HHPOP 1454 non-null int64
TOTPOP 1454 non-null int64
EMPRES 1454 non-null int64
SFDU 1454 non-null int64
MFDU 1454 non-null int64
HHINCQ1 1454 non-null int64
HHINCQ2 1454 non-null int64
HHINCQ3 1454 non-null int64
HHINCQ4 1454 non-null int64
TOTACRE 1454 non-null float64
RESACRE 1454 non-null int64
CIACRE 1454 non-null int64
SHPOP62P 1454 non-null float64
TOTEMP 1454 non-null int64
AGE0004 1454 non-null int64
AGE0519 1454 non-null int64
AGE2044 1454 non-null int64
AGE4564 1454 non-null int64
AGE65P 1454 non-null int64
RETEMPN 1454 non-null int64
FPSEMPN 1454 non-null int64
HEREMPN 1454 non-null int64
OTHEMPN 1454 non-null int64
AGREMPN 1454 non-null int64
MWTEMPN 1454 non-null int64
PRKCST 1454 non-null float64
OPRKCST 1454 non-null float64
area_type 1454 non-null int64
HSENROLL 1454 non-null float64
COLLFTE 1454 non-null float64
COLLPTE 1454 non-null float64
TOPOLOGY 1454 non-null int64
TERMINAL 1454 non-null float64
ZERO 1454 non-null int64
hhlds 1454 non-null int64
sftaz 1454 non-null int64
gqpop 1454 non-null int64
employment_density 1454 non-null float64
total_acres 1454 non-null float64
county_name 1454 non-null object
density_index 1453 non-null float64
household_density 1454 non-null float64
county_id 1454 non-null int64
total_households 1454 non-null int64
total_employment 1454 non-null int64
dtypes: float64(12), int64(36), object(1)
memory usage: 568.0 KB
In [18]:
orca.get_table("households").to_frame().info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100 entries, 2624246 to 558870
Data columns (total 67 columns):
TAZ 100 non-null int64
SERIALNO 100 non-null int64
PUMA5 100 non-null int64
income 100 non-null int64
PERSONS 100 non-null int64
HHT 100 non-null int64
UNITTYPE 100 non-null int64
NOC 100 non-null int64
BLDGSZ 100 non-null int64
TENURE 100 non-null int64
VEHICL 100 non-null int64
hinccat1 100 non-null int64
hinccat2 100 non-null int64
hhagecat 100 non-null int64
hsizecat 100 non-null int64
hfamily 100 non-null int64
hunittype 100 non-null int64
hNOCcat 100 non-null int64
hwrkrcat 100 non-null int64
h0004 100 non-null int64
h0511 100 non-null int64
h1215 100 non-null int64
h1617 100 non-null int64
h1824 100 non-null int64
h2534 100 non-null int64
h3549 100 non-null int64
h5064 100 non-null int64
h6579 100 non-null int64
h80up 100 non-null int64
workers 100 non-null int64
hwork_f 100 non-null int64
hwork_p 100 non-null int64
huniv 100 non-null int64
hnwork 100 non-null int64
hretire 100 non-null int64
hpresch 100 non-null int64
hschpred 100 non-null int64
hschdriv 100 non-null int64
htypdwel 100 non-null int64
hownrent 100 non-null int64
hadnwst 100 non-null int64
hadwpst 100 non-null int64
hadkids 100 non-null int64
bucketBin 100 non-null int64
originalPUMA 100 non-null int64
hmultiunit 100 non-null int64
num_college_age 100 non-null float64
non_workers 100 non-null int64
income_segment 100 non-null category
family 100 non-null bool
num_young_adults 100 non-null float64
household_type 96 non-null object
auto_ownership 100 non-null int64
drivers 100 non-null int64
income_in_thousands 100 non-null float64
home_is_rural 100 non-null bool
work_tour_auto_time_savings 100 non-null int64
num_under16_not_at_school 100 non-null int64
hhsize 100 non-null int64
num_children 100 non-null float64
car_sufficiency 100 non-null int64
non_family 100 non-null bool
num_adolescents 100 non-null float64
no_cars 100 non-null bool
num_young_children 100 non-null float64
home_is_urban 100 non-null bool
home_taz 100 non-null int64
dtypes: bool(5), category(1), float64(6), int64(54), object(1)
memory usage: 49.1 KB
In [19]:
orca.get_table("persons").to_frame().info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 267 entries, 84601 to 6968468
Data columns (total 68 columns):
household_id 267 non-null int64
age 267 non-null int64
RELATE 267 non-null int64
ESR 267 non-null int64
GRADE 267 non-null int64
PNUM 267 non-null int64
PAUG 267 non-null int64
DDP 267 non-null int64
sex 267 non-null int64
WEEKS 267 non-null int64
HOURS 267 non-null int64
MSP 267 non-null int64
POVERTY 267 non-null int64
EARNS 267 non-null int64
pagecat 267 non-null int64
pemploy 267 non-null int64
pstudent 267 non-null int64
ptype 267 non-null int64
padkid 267 non-null int64
has_preschool_kid 267 non-null bool
student_cat 267 non-null object
num_eat_j 267 non-null int64
has_driving_kid 267 non-null bool
home_taz 267 non-null int64
work_and_school_and_worker 267 non-null bool
work_and_school_and_student 267 non-null bool
female 267 non-null bool
has_preschool_kid_at_home 267 non-null bool
has_full_time 267 non-null bool
is_university 267 non-null bool
max_window 267 non-null int64
employed_cat 267 non-null object
student_is_employed 267 non-null bool
is_gradeschool 267 non-null bool
is_highschool 267 non-null bool
has_retiree 267 non-null bool
cdap_activity 267 non-null object
num_main_j 267 non-null int64
num_joint_tours 267 non-null int64
has_non_worker 267 non-null bool
ptype_cat 267 non-null object
is_student 267 non-null bool
under16_not_at_school 267 non-null bool
has_school_kid 267 non-null bool
school_taz 267 non-null float64
is_worker 267 non-null bool
roundtrip_auto_time_to_school 267 non-null float64
distance_to_school 267 non-null float64
age_16_p 267 non-null bool
workplace_taz 267 non-null int64
workplace_in_cbd 267 non-null bool
has_university 267 non-null bool
adult 267 non-null bool
non_mandatory_tour_frequency 164 non-null float64
nonstudent_to_school 267 non-null bool
age_16_to_19 267 non-null bool
num_non_escort_tours 267 non-null float64
mandatory_tour_frequency 80 non-null object
num_shop_j 267 non-null int64
num_mand 267 non-null float64
num_visi_j 267 non-null int64
num_escort_tours 267 non-null float64
roundtrip_auto_time_to_work 267 non-null float64
num_disc_j 267 non-null int64
has_part_time 267 non-null bool
has_school_kid_at_home 267 non-null bool
distance_to_work 267 non-null float64
male 267 non-null bool
dtypes: bool(26), float64(9), int64(28), object(5)
memory usage: 96.5 KB
In [19]:
In [19]:
In [ ]:
Content source: bhargavasana/activitysim
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