Carb

activitysim/abm/models/__init__.py

@@ -35,3 +35,4 @@
 from . import telework_job_sector
 from . import telework_option
 from . import telework
+from . import av

activitysim/abm/models/av.py

@@ -0,0 +1,67 @@
+import pandas as pd
+import numpy as np
+import logging
+
+from activitysim.core import tracing
+from activitysim.core import config
+from activitysim.core import pipeline
+from activitysim.core import inject
+
+from activitysim.core.util import reindex
+
+logger = logging.getLogger(__name__)
+
+@inject.step()
+def autonomous_vehicles(persons_merged, persons, households,
+       skim_dict, skim_stack,
+       chunk_size, trace_hh_id, locutor):
+
+    """
+    Rate-base model for Autonomous vehicles Ownership. 
+
+    Returns:
+    ---------
+    Simulated Autonomous Vehicle ownership
+    """
+    trace_label = 'autonomous_vehicle'
+
+    #Settings
+    model_settings = config.read_model_settings('av.yaml')
+    av_rates_path = config.config_file_path(model_settings['av_rates'])
+    av_rates = pd.read_csv(av_rates_path, index_col = 'year')
+    year = inject.get_injectable('year')
+    scenario = inject.get_injectable('scenario')
+
+    #Choosers
+    households = households.to_frame()
+    persons = persons.to_frame()
+#     persons_merged = persons_merged.to_frame()
+
+    try: 
+        rate = av_rates.loc[year, scenario]
+        vot_reduction = av_rates.loc['vot_reduction',scenario]
+    except KeyError:
+        rate = 0
+        vot_reduction = 0
+
+    logger.info('Simulation year: {}'.format(year))
+    logger.info('Simulation scenario: {}.'.format(scenario))
+    logger.info('Autonomous Vehicles penetration rate:{}'.format(rate))
+    logger.info('Value of time Reduction :{}'.format(vot_reduction))
+
+    av = np.random.choice(np.array([0,1]), size = len(households), p = np.array([1 - rate, rate]))
+
+    households['av'] = av
+    households['hh_value_of_time'] = households['hh_value_of_time'].mask(households.av.astype(bool), 
+                                                                         households['hh_value_of_time']*(1 - vot_reduction))
+    persons['_hh_vot'] = reindex(households.hh_value_of_time, persons.household_id)
+    persons['value_of_time'] = persons['_hh_vot'].where(persons.age>=18, persons._hh_vot * 0.667)
+#     persons_merge['value_of_time'] = persons.value_of_time
+
+    pipeline.replace_table("persons", persons)
+    pipeline.replace_table("households", households)
+
+    if trace_hh_id:
+        tracing.trace_df(persons,
+                         label=trace_label,
+                         warn_if_empty=True)

activitysim/core/mp_tasks.py

@@ -1203,7 +1203,7 @@ def run_multiprocess(run_list, injectables):
     injectables : dict
         dict of values to inject in sub-processes
     """
-
+    
     mem.init_trace(setting('mem_tick'), write_header=True)
     mem.trace_memory_info("run_multiprocess.start")
 

bay_area/configs/settings.yaml

@@ -65,9 +65,6 @@ models:
   - workplace_location
   - auto_ownership_simulate
   - free_parking
-  - telework_job_sector
-  - telework_option
-  - telework
   - cdap_simulate
   - mandatory_tour_frequency
   - mandatory_tour_scheduling
@@ -119,10 +116,6 @@ output_tables:
     - trips
     - joint_tour_participants
     - plans
-#     - trip_mode_choice_raw
-#     - trip_mode_choice_utilities
-#     - trip_mode_choice_specs
-#     - trip_mode_choice_data_dict
 
 # area_types less than this are considered urban
 urban_threshold: 4
@@ -146,7 +139,6 @@ skim_time_periods:
         - EV
 
 # - value of time
-
 # value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot)
 
 min_value_of_time: 1

bay_area/data/.gitignore

@@ -1,3 +1,6 @@
-*
+#Ignore CSV Files
+*.csv
+
+*.omx
 
 !.gitignore

bay_area/simulation.py

@@ -99,6 +99,12 @@ def log_settings():
     parser.add_argument(
         "-k", "--skim_cache", action="store_true",
         help="use skim cache. default is False.")
+    parser.add_argument(
+        "-y", "--year", action="store", 
+        help="year of simulation. default is 2010") 
+    parser.add_argument(
+        "-s", "--scenario", action="store", 
+        help="scenario. One of 'conservative', 'core', or 'aggressive'. Default is 'core' ") 
 
     args = parser.parse_args()
 
@@ -127,6 +133,12 @@ def log_settings():
     injectables = ['data_dir', 'configs_dir', 'output_dir']
     inject.add_injectable('data_dir', 'data')
     inject.add_injectable('configs_dir', ['configs', 'configs/configs'])
+
+    #Adding scenario arguments
+    year = args.year if args.year else 2010
+    scenario = args.scenario if args.scenario else 'core'
+    inject.add_injectable('year', args.year)
+    inject.add_injectable('scenario', args.scenario)
 
     config.filter_warnings()
     tracing.config_logger()

carb/bay_area_base/configs/annotate_job_sector.csv

@@ -0,0 +1,4 @@
+Description,Target,Expression
+#,,Annotate persons categories
+education_category,education_category,"pd.cut(df.edu,[-1,20,21,25], labels = [0,1,2]).astype(int)"
+workers_category,workers_category,"pd.cut(df.num_workers, [-1,2,20], labels = [0,1]).astype(int)"

carb/bay_area_base/configs/annotate_telework_option.csv

@@ -0,0 +1,4 @@
+Description,Target,Expression
+#,,Annotate persons categories
+age_category,age_category,"pd.cut(df.age, [-np.inf,25,45,np.inf], labels = [0,1,2]).astype(int)"
+income_category,income_category,"pd.cut(df.income, [-np.inf,50000,150000,np.inf], labels = [0,1,2]).astype(int)"

carb/bay_area_base/configs/av.yaml

@@ -0,0 +1 @@
+av_rates: av_rates.csv

carb/bay_area_base/configs/av_rates.csv

@@ -0,0 +1,8 @@
+year,conservative,core,aggressive
+2025,0.0,0.0,0.0
+2030,0.0,0.0,0.0
+2035,0.0,0.0,0.0
+2040,0.0,0.0,0.0
+2045,0.0,0.0,0.0
+2050,0.0,0.0,0.0
+vot_reduction,0.0,0.0,0.0

carb/bay_area_base/configs/settings.yaml

@@ -40,16 +40,16 @@ mem_tick: 0
 use_shadow_pricing: False
 
 # sample size
-households_sample_size: 0
+households_sample_size: 100
 
 # multiprocessing controls
 multiprocess: True
-chunk_size: 10_000_000_000
-num_processes: 94
+chunk_size: 10_000_000
+num_processes: 2
 stagger: 0
 
 # tracing
-trace_hh_id: 456291
+trace_hh_id: 
 trace_od:
 
 # to resume after last successful checkpoint, specify resume_after: _
@@ -61,10 +61,14 @@ models:
   - compute_accessibility
   - initialize_households
   ### mp_households step
+  - autonomous_vehicles
   - school_location
   - workplace_location
   - auto_ownership_simulate
   - free_parking
+  - telework_job_sector
+  - telework_option
+  - telework
   - cdap_simulate
   - mandatory_tour_frequency
   - mandatory_tour_scheduling
@@ -96,7 +100,7 @@ multiprocess_steps:
   - name: mp_initialize
     begin: initialize_landuse
   - name: mp_households
-    begin: school_location
+    begin: autonomous_vehicles
     slice:
       tables:
         - households

carb/bay_area_base/data/.gitignore

@@ -1,3 +1,6 @@
-*
+#Ignore CSV Files
+*.csv
+
+*.omx
 
 !.gitignore

carb/bay_area_base/simulation.py

@@ -18,6 +18,7 @@
 
 logger = logging.getLogger('activitysim')
 
+
 def cleanup_output_files():
 
     active_log_files = \
@@ -98,6 +99,12 @@ def log_settings():
     parser.add_argument(
         "-k", "--skim_cache", action="store_true",
         help="use skim cache. default is False.")
+    parser.add_argument(
+        "-y", "--year", action="store", 
+        help="year of simulation. default is 2010") 
+    parser.add_argument(
+        "-s", "--scenario", action="store", 
+        help="scenario. One of 'conservative', 'core', or 'aggressive'. Default is 'core' ") 
 
     args = parser.parse_args()
 
@@ -126,6 +133,12 @@ def log_settings():
     injectables = ['data_dir', 'configs_dir', 'output_dir']
     inject.add_injectable('data_dir', 'data')
     inject.add_injectable('configs_dir', ['configs', 'configs/configs'])
+
+    #Adding scenario arguments
+    year = args.year if args.year else 2010
+    scenario = args.scenario if args.scenario else 'core'
+    inject.add_injectable('year', args.year)
+    inject.add_injectable('scenario', args.scenario)
 
     config.filter_warnings()
     tracing.config_logger()

carb/bay_area_base/tmp/block_sfbay.cpg

@@ -0,0 +1 @@
+ISO-8859-1

carb/bay_area_base/tmp/block_sfbay.prj

@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

carb/bay_area_base/tmp/taz_sfbay.cpg

@@ -0,0 +1 @@
+ISO-8859-1

carb/bay_area_base/tmp/taz_sfbay.prj

@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

carb/final_scenarios/baseline/README.MD

@@ -0,0 +1,4 @@
+
+### Example
+
+The primary ActivitySim example model.  See https://activitysim.github.io/activitysim for more information.

carb/final_scenarios/baseline/configs/annotate_households.csv

@@ -0,0 +1,34 @@
+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_in_thousands,income_in_thousands,(households.income / 1000).clip(lower=0)
+income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, 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,households.workers
+number of non_workers,num_non_workers,households.hhsize - households.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('adult', persons, households)"
+num_children,num_children,"_PERSON_COUNT('~adult', 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_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(constants.HHT_NONFAMILY)
+family,family,households.HHT.isin(constants.HHT_FAMILY)
+home_is_urban,home_is_urban,"reindex(land_use.area_type, households.TAZ) < setting('urban_threshold')"
+home_is_rural,home_is_rural,"reindex(land_use.area_type, households.TAZ) > setting('rural_threshold')"
+#,, default for work and school location logsums before auto_ownership model is run
+,auto_ownership,households.VEHICL
+#home_taz,home_taz,households.TAZ

carb/final_scenarios/baseline/configs/annotate_job_sector.csv

@@ -0,0 +1,4 @@
+Description,Target,Expression
+#,,Annotate persons categories
+education_category,education_category,"pd.cut(df.edu,[-1,20,21,25], labels = [0,1,2]).astype(int)"
+workers_category,workers_category,"pd.cut(df.num_workers, [-1,2,20], labels = [0,1]).astype(int)"

carb/final_scenarios/baseline/configs/annotate_landuse.csv

@@ -0,0 +1,3 @@
+Description,Target,Expression
+#,, annotate landuse table after import
+density_index,density_index,(land_use.hh_density * land_use.employment_density) / (land_use.hh_density + land_use.employment_density).clip(lower=1)

carb/final_scenarios/baseline/configs/annotate_telework_option.csv

@@ -0,0 +1,4 @@
+Description,Target,Expression
+#,,Annotate persons categories
+age_category,age_category,"pd.cut(df.age, [-np.inf,25,45,np.inf], labels = [0,1,2]).astype(int)"
+income_category,income_category,"pd.cut(df.income, [-np.inf,50000,150000,np.inf], labels = [0,1,2]).astype(int)"

carb/final_scenarios/baseline/configs/av.yaml

@@ -0,0 +1 @@
+av_rates: av_rates.csv

carb/final_scenarios/baseline/configs/av_rates.csv

@@ -0,0 +1,8 @@
+year,conservative,core,aggressive
+2025,0.0,0.0,0.0
+2030,0.0,0.0,0.0
+2035,0.0,0.0,0.0
+2040,0.0,0.0,0.0
+2045,0.0,0.0,0.0
+2050,0.0,0.0,0.0
+vot_reduction,0.0,0.0,0.0