Merge 8b4f081 into f189143

.travis.yml

@@ -19,24 +19,27 @@ jobs:
     - env: TEST_SUITE=activitysim/core
 
     - stage: Examples
-      name: "Prototype MTC"
-      env: TEST_SUITE=activitysim/examples/prototype_mtc/test
-    - name: "Prototype MTC (Extended)"
-      env: TEST_SUITE=activitysim/examples/prototype_mtc_extended/test
-    - name: "Multizone Example"
-      env: TEST_SUITE=activitysim/examples/placeholder_multiple_zone/test
-    - name: "Marin Example"
-      env: TEST_SUITE=activitysim/examples/prototype_marin/test
-    - name: "ARC Example"
-      env: TEST_SUITE=activitysim/examples/prototype_arc/test
-    - name: "SEMCOG Example"
-      env: TEST_SUITE=activitysim/examples/prototype_semcog/test
-    - name: "PSRC Example"
-      env: TEST_SUITE=activitysim/examples/placeholder_psrc/test
-    - name: "SANDAG Example"
-      env: TEST_SUITE=activitysim/examples/placeholder_sandag/test
-    - name: "SANDAG Cross-Border Example"
-      env: TEST_SUITE=activitysim/examples/prototype_sandag_xborder/test
+      name: "MWCOG Example"
+      env: TEST_SUITE=activitysim/examples/prototype_mwcog/test
+    #- name: "Prototype MTC"
+    #  env: TEST_SUITE=activitysim/examples/prototype_mtc/test
+    #- name: "Prototype MTC (Extended)"
+    #  env: TEST_SUITE=activitysim/examples/prototype_mtc_extended/test
+    #- name: "Multizone Example"
+    #  env: TEST_SUITE=activitysim/examples/placeholder_multiple_zone/test
+    #- name: "Marin Example"
+    #  env: TEST_SUITE=activitysim/examples/prototype_marin/test
+    #- name: "ARC Example"
+    #  env: TEST_SUITE=activitysim/examples/prototype_arc/test
+    #- name: "SEMCOG Example"
+    #  env: TEST_SUITE=activitysim/examples/prototype_semcog/test
+    #- name: "PSRC Example"
+    #  env: TEST_SUITE=activitysim/examples/placeholder_psrc/test
+    #- name: "SANDAG Example"
+    #  env: TEST_SUITE=activitysim/examples/placeholder_sandag/test
+    #- name: "SANDAG Cross-Border Example"
+    #  env: TEST_SUITE=activitysim/examples/prototype_sandag_xborder/test
+
 
     - stage: Estimation Mode
       name: "Larch Test"

activitysim/examples/example_manifest.yaml

@@ -894,3 +894,19 @@
     - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_xborder/traffic_skims_xborder_PM.omx
       data/traffic_skims_xborder_PM.omx
       87544b59488c4ca654ae8d9756dfb3a8226b85faa21e631339d86fae5dc60feb
+
+- name: prototype_mwcog
+  description: 53 zone test example for the MWCOG region
+  # activitysim create -e prototype_mwcog -d test_prototype_mwcog
+  # cd test_prototype_mwcog
+  # python simulation.py -c configs -o output -d data
+  # cd ..
+  include:
+    - prototype_mwcog/extensions
+    - prototype_mwcog/data
+    - prototype_mwcog/configs
+    - prototype_mwcog/configs_mp
+    - prototype_mwcog/output
+    - prototype_mwcog/README.MD
+    - prototype_mwcog/simulation.py
+

activitysim/examples/prototype_mwcog/.gitignore

@@ -0,0 +1,2 @@
+data_*/
+output_*/

activitysim/examples/prototype_mwcog/README.MD

@@ -0,0 +1,3 @@
+### MWCOG Example
+
+This is an example of the Metropolitan Washington Council of Governments ActivitySim model. This is a 53 zone portion of the model, centered on the National Mall. 

activitysim/examples/prototype_mwcog/configs/_dummy_coefficients.csv

@@ -0,0 +1,2 @@
+coefficient_name,value,constrain
+coef_one,1,T

activitysim/examples/prototype_mwcog/configs/accessibility.csv

@@ -0,0 +1,59 @@
+Description,Target,Expression
+#,,
+#,, auto peak
+#,,
+#,, assume peak occurs in AM for outbound and PM for inbound
+peak round trip time,_auPkTime,"skim_od[('SOV_TIME', 'AM')] + skim_do[('SOV_TIME', 'PM')]"
+decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile)
+auto peak retail,auPkRetail,df.RETEMP * _decay
+auto peak total,auPkTotal,df.TOTEMP * _decay
+#,,
+#,, auto off-peak
+#,,
+#,, assume midday occurs entirely in the midday period
+off-peak round trip time,_auOpTime,"skim_od[('SOV_TIME', 'MD')] + skim_do[('SOV_TIME', 'MD')]"
+decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile)
+auto off-peak retail,auOpRetail,df.RETEMP * _decay
+auto off-peak total,auOpTotal,df.TOTEMP * _decay
+#,,
+#,, transit peak
+#,,
+#,, assume peak outbound transit occurs in AM
+o-d peak transit ivt,_inVehicleTime,"skim_od[('WK_TRN_WK_TOTIVT', 'AM')]"
+o-d peak transit ovt,_outOfVehicleTime,"skim_od[('WK_TRN_WK_IWAIT', 'AM')] + skim_od[('WK_TRN_WK_XWAIT', 'AM')] + skim_od[('WK_TRN_WK_WACC_EGR', 'AM')] + skim_od[('WK_TRN_WK_WAUX', 'AM')]"
+o-d peak transit time,_trPkTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime)
+#,, assume peak inbound transit occurs in PM
+d-o peak transit ivt,_inVehicleTime,"skim_od[('WK_TRN_WK_TOTIVT', 'PM')]"
+d-o peak transit ovt,_outOfVehicleTime,"skim_od[('WK_TRN_WK_IWAIT', 'PM')] + skim_od[('WK_TRN_WK_XWAIT', 'PM')] + skim_od[('WK_TRN_WK_WACC_EGR', 'PM')] + skim_od[('WK_TRN_WK_WAUX', 'PM')]"
+d-o peak transit time,_trPkTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime)
+peak transit time,_trPkTime,_trPkTime_od + _trPkTime_do
+round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0)
+decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit)
+transit peak retail,trPkRetail,df.RETEMP * _decay
+transit peak total,trPkTotal,df.TOTEMP * _decay
+#,,
+#,, transit off-peak
+#,,
+#,, assume off-peak outbound transit occurs in the MD time period
+o-d off-peak transit ivt,_inVehicleTime,"skim_od[('WK_TRN_WK_TOTIVT', 'MD')]"
+o-d off-peak transit ovt,_outOfVehicleTime,"skim_od[('WK_TRN_WK_IWAIT', 'MD')] + skim_od[('WK_TRN_WK_XWAIT', 'MD')] + skim_od[('WK_TRN_WK_WACC_EGR', 'MD')] + skim_od[('WK_TRN_WK_WAUX', 'MD')]"
+o-d off-peak transit time,_trOpTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime)
+#,, assume off-peak inbound transit occurs in the MD time period
+d-o off-peak transit ivt,_inVehicleTime,"skim_do[('WK_TRN_WK_TOTIVT', 'MD')]"
+d-o off-peak transit ovt,_outOfVehicleTime,"skim_do[('WK_TRN_WK_IWAIT', 'MD')] + skim_do[('WK_TRN_WK_XWAIT', 'MD')] + skim_do[('WK_TRN_WK_WACC_EGR', 'MD')] + skim_do[('WK_TRN_WK_WAUX', 'MD')]"
+d-o off-peak transit time,_trOpTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime)
+peak transit time,_trOpTime,_trOpTime_od + _trOpTime_do
+#,,FIXME - _rt_available calculation appears to be wrong in mtctm1 accessibility.job
+#round trip path is available,_rt_available,(_trOpTime > 0)
+round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0)
+decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit)
+transit off-peak retail,trOpRetail,df.RETEMP * _decay
+transit off-peak total,trOpTotal,df.TOTEMP * _decay
+#,,
+#,, non motorized
+#,,
+non-motorized round trip distance,_nmDist,skim_od['DISTWALK'] + skim_do['DISTWALK']
+round trip path is available,_rt_available,_nmDist <= maximum_walk_distance
+decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk)
+retail accessibility,nmRetail,df.RETEMP * _decay
+total accessibility,nmTotal,df.TOTEMP * _decay

activitysim/examples/prototype_mwcog/configs/accessibility.yaml

@@ -0,0 +1,13 @@
+
+# columns from land_use table to add to df
+land_use_columns: ['RETEMP', 'TOTEMP']
+
+CONSTANTS:
+  # dispersion parameters
+  dispersion_parameter_automobile: -0.05
+  dispersion_parameter_transit: -0.05
+  dispersion_parameter_walk: -1.00
+  # maximum walk distance in miles
+  maximum_walk_distance: 3.0
+  # perceived minute of in-vehicle time for every minute of out-of-vehicle time
+  out_of_vehicle_time_weight: 2.0

activitysim/examples/prototype_mwcog/configs/annotate_households.csv

@@ -0,0 +1,42 @@
+Description,Target,Expression
+#,, annotate households table after import
+,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)"
+#,,FIXME households.income can be negative  - so we clip?
+income,income,households.hhincadj.fillna(0)
+income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0)
+income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 50, 100, 150, np.inf], labels=[1, 2, 3, 4]).astype(int)"
+#,,
+,_MIN_VOT,setting('min_value_of_time')
+,_MAX_VOT,setting('max_value_of_time')
+,_MU,setting('distributed_vot_mu')
+,_SIGMA,setting('distributed_vot_sigma')
+median_value_of_time,median_value_of_time,"income_segment.map({k: v for k, v in setting('household_median_value_of_time').items()})"
+hh_value_of_time,hh_value_of_time,"rng.lognormal_for_df(df, mu=np.log(median_value_of_time * _MU), sigma=_SIGMA).clip(_MIN_VOT, _MAX_VOT)"
+#,,
+#num_workers was renamed in import,,
+,num_workers,"_PERSON_COUNT('(ESR==1)|(ESR==2)|(ESR==4)|(ESR==5)', persons, households)"
+number of non_workers,num_non_workers,households.hhsize - num_workers
+#,,
+#,,we assume that everyone 16 and older is a potential driver
+number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)"
+num_adults,num_adults,"_PERSON_COUNT('18 <= age', persons, households)"
+num_children,num_children,"_PERSON_COUNT('18 > age', persons, households)"
+num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)"
+num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)"
+num_children_6_to_12,num_children_6_to_12,"_PERSON_COUNT('6 <= age <= 12', persons, households)"
+num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)"
+num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)"
+num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)"
+non_family,non_family,households.HHT.isin(HHT_NONFAMILY)
+family,family,households.HHT.isin(HHT_FAMILY)
+home_is_urban,home_is_urban,"reindex(land_use.AREATYPE, households.home_zone_id) < setting('urban_threshold')"
+home_is_rural,home_is_rural,"reindex(land_use.AREATYPE, households.home_zone_id) > setting('rural_threshold')"
+#,, default for work and school location logsums before auto_ownership model is run
+,auto_ownership,households.auto_ownership
+TAZ column to match settings file,TAZ,households.home_zone_id
+number of pre-driving age children in the household,num_predrive_child,"_PERSON_COUNT('ptype == 7', persons, households)"
+number of non-working adult in the household,num_nonworker_adults,"_PERSON_COUNT('ptype == 4', persons, households)"
+number of full time workers,num_fullTime_workers,"_PERSON_COUNT('is_fulltime_worker', persons, households)"
+number of part time workers,num_partTime_workers,"_PERSON_COUNT('is_parttime_worker', persons, households)"
+number of retired adults in the household,_num_retired_adults,"_PERSON_COUNT('ptype == 5', persons, households)"
+Retired Adults Only Households,retired_adults_only_hh,(households.hhsize > 0) & (households.hhsize == _num_retired_adults)

activitysim/examples/prototype_mwcog/configs/annotate_households_cdap.csv

@@ -0,0 +1,9 @@
+Description,Target,Expression
+#,, annotate households table after cdap model has run
+num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
+num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults
+num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers
+participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0)

activitysim/examples/prototype_mwcog/configs/annotate_households_workplace.csv

@@ -0,0 +1,5 @@
+Description,Target,Expression
+#,, annotate households table after workplace_location model has run
+#,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio
+,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0)
+#,,handle persons with no locatcion

activitysim/examples/prototype_mwcog/configs/annotate_landuse.csv

@@ -0,0 +1,9 @@
+Description,Target,Expression
+#,, annotate landuse table after import
+household_density,household_density,land_use.HH / (land_use.LANDAREA * 640)
+employment_density,employment_density,land_use.TOTEMP / (land_use.LANDAREA * 640)
+,employment_density,employment_density.fillna(0)
+density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1)
+topology (assume flat),TOPOLOGY,1
+zero out land use for zero area,Park_Acres,"np.where(land_use.LANDAREA==0,0,land_use.Park_Acres)"
+zero out land use for zero area,GC_Acres,"np.where(land_use.LANDAREA==0,0,land_use.GC_Acres)"

activitysim/examples/prototype_mwcog/configs/annotate_persons.csv

@@ -0,0 +1,68 @@
+Description,Target,Expression
+#,, annotate persons table after import
+age_0_to_5,age_0_to_5,"persons.age.between(0,5)"
+age_6_to_12,age_6_to_12,"persons.age.between(6,12)"
+age_16_to_19,age_16_to_19,"persons.age.between(16, 19)"
+age_16_p,age_16_p,persons.age >= 16
+adult,adult,persons.age >= 18
+young,young,persons.age <= 25
+old,old,persons.age >= 65
+male,male,persons.SEX == 1
+female,female,persons.SEX == 2
+,esr,persons.ESR.fillna(0)
+,wkhp,persons.WKHP.fillna(0)
+,wkw,persons.WKW.fillna(0)
+,schg,persons.SCHG.fillna(0)
+,mil,persons.MIL.fillna(0)
+employment status type,pemploy,np.zeros(len(persons))
+,pemploy,"np.where(persons.age < 16, PEMPLOY_CHILD, PEMPLOY_PART)"
+,pemploy,"np.where((persons['age'] >= 16) & ((esr == 3) | (esr == 6)), PEMPLOY_NOT, pemploy)"
+,pemploy,"np.where((persons.age>=16)&((esr != 3)&(esr != 6))&(wkhp >= 35) & (wkw >= 1) & (wkw <= 4), PEMPLOY_FULL, pemploy)"
+student category,pstudent,np.zeros(len(persons))
+,pstudent,"np.where((pemploy == 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)"
+,pstudent,"np.where((pemploy == 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((schg < 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)"
+,pstudent,"np.where((schg < 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((schg >= 15) & (persons.age >= 16) & (pemploy != 1), PSTUDENT_UNIVERSITY, pstudent)"
+,pstudent,"np.where((schg >= 15) & (persons.age < 16) & (pemploy != 1), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((persons.age <= 19) & (pemploy != 1) & (schg >=1) & (schg<=14), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((persons.age > 19) & (pemploy != 1) & (schg >=1) & (schg<=14),  PSTUDENT_UNIVERSITY, pstudent)"
+,pstudent,"np.where(pstudent == 0, 3, pstudent)"
+person type,ptype,np.zeros(len(persons))
+,ptype,"np.where((pemploy == 1),  PTYPE_FULL, PTYPE_NONWORK)"
+,ptype,"np.where((pstudent == 3) & (pemploy == 2), PTYPE_PART, ptype)"
+,ptype,"np.where((pstudent == 3) & (persons['age'] >= 65) & ((pemploy == 3) | (pemploy == 4)), PTYPE_RETIRED, ptype)"
+,ptype,"np.where((pstudent == 3) & (persons['age'] < 6) & ((pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)"
+,ptype,"np.where((pstudent == 3) & (persons['age'] >= 6) & (persons['age'] <= 64) & ((pemploy == 3) | (pemploy == 4)), PTYPE_NONWORK, ptype)"
+,ptype,"np.where((pstudent == 2)  & ((pemploy == 2)  | (pemploy == 3) | (pemploy == 4)), PTYPE_UNIVERSITY, ptype)"
+,ptype,"np.where((pstudent == 1) & (persons['age'] < 6)  & ((pemploy == 2)  | (pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)"
+,ptype,"np.where((pstudent == 1) & (persons['age'] >= 16)  & ((pemploy == 2)  | (pemploy == 3) | (pemploy == 4)), PTYPE_DRIVING, ptype)"
+,ptype,"np.where((pstudent == 1) & (persons['age'] >= 6) & (persons['age'] < 16)  & ((pemploy == 2)  | (pemploy == 3) | (pemploy == 4)), PTYPE_SCHOOL, ptype)"
+presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, ptype == PTYPE_NONWORK)"
+presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, ptype == PTYPE_RETIRED)"
+presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, ptype == PTYPE_PRESCHOOL)"
+presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, ptype == PTYPE_DRIVING)"
+presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, ptype == PTYPE_SCHOOL)"
+presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, pemploy==PEMPLOY_FULL)"
+presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, pemploy==PEMPLOY_PART)"
+presence of university student other than self in household,has_university,"other_than(persons.household_id, ptype == PTYPE_UNIVERSITY)"
+student_is_employed,student_is_employed,"np.where(((ptype == PTYPE_UNIVERSITY) | (ptype == PTYPE_DRIVING)) & ((pemploy == PEMPLOY_FULL) | (pemploy == PEMPLOY_PART)), True, False)"
+nonstudent_to_school,nonstudent_to_school,"np.where(((ptype ==  PTYPE_FULL) | (ptype ==   PTYPE_PART) | (ptype ==  PTYPE_NONWORK) | (ptype ==  PTYPE_RETIRED))  & ((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent ==  PSTUDENT_UNIVERSITY)), True, False)"
+is_student,is_student,"np.where((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY), True, False)"
+preschool age can go to preschool,is_student,"np.where((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY) & (persons.age > GRADE_SCHOOL_MIN_AGE), True, is_student)"
+is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE)
+is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE)
+is_university,is_university,pstudent == PSTUDENT_UNIVERSITY
+school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)"
+school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)"
+school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)"
+#,,
+is_worker,is_worker,"np.where((pemploy == PEMPLOY_FULL) |( pemploy == PEMPLOY_PART), True, False)"
+#,,
+home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)"
+person number,PNUM,persons.per_num
+income,income,"reindex(households.hhincadj, persons.household_id)"
+income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0)
+income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 50, 100, 150, np.inf], labels=[1, 2, 3, 4]).astype(int)"
+is_fulltime_worker,is_fulltime_worker,"((age_16_p) & (wkhp >=35) & (wkw>=1) & (wkw<=4) & (~esr.isin([3,6])))"
+is_parttime_worker,is_parttime_worker,"((age_16_p) & (~esr.isin([3,6])) & (is_fulltime_worker == False))"