diff --git a/activitysim/abm/models/settings_checker.py b/activitysim/abm/models/settings_checker.py
index 907e7e999..2c387fdcf 100644
--- a/activitysim/abm/models/settings_checker.py
+++ b/activitysim/abm/models/settings_checker.py
@@ -322,7 +322,7 @@ def try_load_model_settings(
 ) -> tuple[PydanticBase | None, Exception | None]:
 
     msg = f"Attempting to load model settings for {model_name} via {model_settings_class.__name__} and {model_settings_file}"
-    logger.info(msg)
+    logger.debug(msg)
     file_logger.info(msg)
 
     try:
@@ -348,7 +348,7 @@ class InputCheckerSettings(PydanticBase):
             )
         result = model_settings, None
         msg = f"Successfully loaded model settings from {model_settings_file}"
-        logger.info(msg)
+        logger.debug(msg)
     except Exception as e:
         result = None, e
     return result
@@ -358,12 +358,12 @@ def try_load_spec(
     model_name: str, model_settings: PydanticBase, spec_file: str, state: State
 ) -> tuple[DataFrame | None, Exception | None]:
     msg = f"Attempting to load SPEC for {model_name} via {model_settings.__class__.__name__}"
-    logger.info(msg)
+    logger.debug(msg)
     file_logger.info(msg)
     try:
         result = state.filesystem.read_model_spec(spec_file), None
         msg = f"Successfully loaded model SPEC from {spec_file}"
-        logger.info(msg)
+        logger.debug(msg)
         file_logger.info(msg)
     except Exception as e:
         # always return a dataframe
@@ -375,13 +375,13 @@ def try_load_coefs(
     model_name: str, model_settings: PydanticBase, coefs_file: str, state: State
 ) -> tuple[DataFrame, Optional[Exception]]:
     msg = f"Attempting to load COEFFICIENTS for {model_name} via {model_settings.__class__.__name__}"
-    logger.info(msg)
+    logger.debug(msg)
     file_logger.info(msg)
 
     try:
         result = state.filesystem.read_model_coefficients(file_name=coefs_file), None
         msg = f"Successfully loaded model Coefficients from {coefs_file}"
-        logger.info(msg)
+        logger.debug(msg)
         file_logger.info(msg)
     except Exception as e:
         result = None, e
@@ -423,7 +423,7 @@ def try_eval_spec_coefs(
         else:
             result = eval_coefficients(state, spec, coefs, estimator=None), None
         msg = f"Successfully evaluated coefficients for {model_name}"
-        logger.info(msg)
+        logger.debug(msg)
         file_logger.info(msg)
     except Exception as e:
         result = None, e
diff --git a/activitysim/abm/models/trip_destination.py b/activitysim/abm/models/trip_destination.py
index 6584134ef..c07b9ede3 100644
--- a/activitysim/abm/models/trip_destination.py
+++ b/activitysim/abm/models/trip_destination.py
@@ -820,7 +820,7 @@ def compute_logsums(
         adds od_logsum and dp_logsum columns to trips (in place)
     """
     trace_label = tracing.extend_trace_label(trace_label, "compute_logsums")
-    logger.info("Running %s with %d samples", trace_label, destination_sample.shape[0])
+    logger.debug("Running %s with %d samples", trace_label, destination_sample.shape[0])
 
     # chunk usage is uniform so better to combine
     chunk_tag = "trip_destination.compute_logsums"
@@ -977,7 +977,7 @@ def trip_destination_simulate(
 
     alt_dest_col_name = model_settings.ALT_DEST_COL_NAME
 
-    logger.info("Running trip_destination_simulate with %d trips", len(trips))
+    logger.debug("Running trip_destination_simulate with %d trips", len(trips))
 
     skims = skim_hotel.sample_skims(presample=False)
 
@@ -1102,7 +1102,7 @@ def choose_trip_destination(
         )
         trips = trips[~dropped_trips]
 
-    t0 = print_elapsed_time("%s.trip_destination_sample" % trace_label, t0)
+    t0 = print_elapsed_time("%s.trip_destination_sample" % trace_label, t0, debug=True)
 
     if trips.empty:
         return pd.Series(index=trips.index).to_frame("choice"), None
@@ -1124,7 +1124,7 @@ def choose_trip_destination(
         destination_sample["od_logsum"] = 0.0
         destination_sample["dp_logsum"] = 0.0
 
-    t0 = print_elapsed_time("%s.compute_logsums" % trace_label, t0)
+    t0 = print_elapsed_time("%s.compute_logsums" % trace_label, t0, debug=True)
 
     destinations = trip_destination_simulate(
         state,
@@ -1155,7 +1155,9 @@ def choose_trip_destination(
     else:
         destination_sample = None
 
-    t0 = print_elapsed_time("%s.trip_destination_simulate" % trace_label, t0)
+    t0 = print_elapsed_time(
+        "%s.trip_destination_simulate" % trace_label, t0, debug=True
+    )
 
     return destinations, destination_sample
 
@@ -1484,7 +1486,9 @@ def run_trip_destination(
             else:
                 None
 
-            logger.info("Running %s with %d trips", nth_trace_label, nth_trips.shape[0])
+            logger.debug(
+                "Running %s with %d trips", nth_trace_label, nth_trips.shape[0]
+            )
 
             # - choose destination for nth_trips, segmented by primary_purpose
             choices_list = []
@@ -1649,7 +1653,7 @@ def trip_destination(
         estimator.write_table(state.get_dataframe("land_use"), "landuse", append=False)
         estimator.write_model_settings(model_settings, model_settings_file_name)
 
-    logger.info("Running %s with %d trips", trace_label, trips_df.shape[0])
+    logger.debug("Running %s with %d trips", trace_label, trips_df.shape[0])
 
     trips_df, save_sample_df = run_trip_destination(
         state,
diff --git a/activitysim/abm/models/util/canonical_ids.py b/activitysim/abm/models/util/canonical_ids.py
index ab2623916..e3246a623 100644
--- a/activitysim/abm/models/util/canonical_ids.py
+++ b/activitysim/abm/models/util/canonical_ids.py
@@ -255,7 +255,7 @@ def canonical_tours(state: workflow.State):
     )
     non_mandatory_channels = enumerate_tour_types(non_mandatory_tour_flavors)
 
-    logger.info(f"Non-Mandatory tour flavors used are {non_mandatory_tour_flavors}")
+    logger.debug(f"Non-Mandatory tour flavors used are {non_mandatory_tour_flavors}")
 
     # ---- mandatory_channels
     mtf_model_settings_file_name = "mandatory_tour_frequency.yaml"
@@ -271,7 +271,7 @@ def canonical_tours(state: workflow.State):
     )
     mandatory_channels = enumerate_tour_types(mandatory_tour_flavors)
 
-    logger.info(f"Mandatory tour flavors used are {mandatory_tour_flavors}")
+    logger.debug(f"Mandatory tour flavors used are {mandatory_tour_flavors}")
 
     # ---- atwork_subtour_channels
     atwork_model_settings_file_name = "atwork_subtour_frequency.yaml"
@@ -288,7 +288,7 @@ def canonical_tours(state: workflow.State):
     )
     atwork_subtour_channels = enumerate_tour_types(atwork_subtour_flavors)
 
-    logger.info(f"Atwork subtour flavors used are {atwork_subtour_flavors}")
+    logger.debug(f"Atwork subtour flavors used are {atwork_subtour_flavors}")
 
     # we need to distinguish between subtours of different work tours
     # (e.g. eat1_1 is eat subtour for parent work tour 1 and eat1_2 is for work tour 2)
@@ -317,7 +317,7 @@ def canonical_tours(state: workflow.State):
     joint_tour_flavors = determine_flavors_from_alts_file(
         jtf_alts, provided_joint_flavors, default_joint_flavors
     )
-    logger.info(f"Joint tour flavors used are {joint_tour_flavors}")
+    logger.debug(f"Joint tour flavors used are {joint_tour_flavors}")
 
     joint_tour_channels = enumerate_tour_types(joint_tour_flavors)
     joint_tour_channels = ["j_%s" % c for c in joint_tour_channels]
@@ -343,7 +343,7 @@ def canonical_tours(state: workflow.State):
         school_escort_flavors = {"escort": 2 * num_escortees}
         school_escort_channels = enumerate_tour_types(school_escort_flavors)
         school_escort_channels = ["se_%s" % c for c in school_escort_channels]
-        logger.info(f"School escort tour flavors used are {school_escort_flavors}")
+        logger.debug(f"School escort tour flavors used are {school_escort_flavors}")
 
         sub_channels = sub_channels + school_escort_channels
 
diff --git a/activitysim/abm/models/util/tour_destination.py b/activitysim/abm/models/util/tour_destination.py
index 2032d21b9..d99803bd7 100644
--- a/activitysim/abm/models/util/tour_destination.py
+++ b/activitysim/abm/models/util/tour_destination.py
@@ -92,18 +92,18 @@ def _destination_sample(
         coefficients_file_name=model_settings.COEFFICIENTS,
     )
 
-    logger.info("running %s with %d tours", trace_label, len(choosers))
+    logger.debug("running %s with %d tours", trace_label, len(choosers))
 
     sample_size = model_settings.SAMPLE_SIZE
     if estimator and model_settings.ESTIMATION_SAMPLE_SIZE >= 0:
         sample_size = model_settings.ESTIMATION_SAMPLE_SIZE
-        logger.info(
+        logger.debug(
             f"Estimation mode for {trace_label} using sample size of {sample_size}"
         )
 
     if state.settings.disable_destination_sampling:
         sample_size = 0
-        logger.info(
+        logger.debug(
             f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set"
         )
 
@@ -562,7 +562,7 @@ def destination_presample(
     trace_label = tracing.extend_trace_label(trace_label, "presample")
     chunk_tag = "tour_destination.presample"
 
-    logger.info(f"{trace_label} location_presample")
+    logger.debug(f"{trace_label} location_presample")
 
     alt_dest_col_name = model_settings.ALT_DEST_COL_NAME
     assert DEST_TAZ != alt_dest_col_name
@@ -652,7 +652,7 @@ def run_destination_sample(
         )
 
     if pre_sample_taz:
-        logger.info(
+        logger.debug(
             "Running %s destination_presample with %d tours" % (trace_label, len(tours))
         )
 
@@ -740,7 +740,7 @@ def run_destination_logsums(
         how="left",
     )
 
-    logger.info("Running %s with %s rows", trace_label, len(choosers))
+    logger.debug("Running %s with %s rows", trace_label, len(choosers))
 
     state.tracing.dump_df(DUMP, persons_merged, trace_label, "persons_merged")
     state.tracing.dump_df(DUMP, choosers, trace_label, "choosers")
@@ -832,7 +832,7 @@ def run_destination_simulate(
 
     constants = model_settings.CONSTANTS
 
-    logger.info("Running tour_destination_simulate with %d persons", len(choosers))
+    logger.debug("Running tour_destination_simulate with %d persons", len(choosers))
 
     # create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers
     # and a zone_id in the alternatives which get merged during interaction
diff --git a/activitysim/abm/models/util/tour_od.py b/activitysim/abm/models/util/tour_od.py
index a74309be8..5ec9dd493 100644
--- a/activitysim/abm/models/util/tour_od.py
+++ b/activitysim/abm/models/util/tour_od.py
@@ -155,7 +155,7 @@ def _od_sample(
     else:
         alt_col_name = alt_od_col_name
 
-    logger.info("running %s with %d tours", trace_label, len(choosers))
+    logger.debug("running %s with %d tours", trace_label, len(choosers))
 
     sample_size = model_settings.SAMPLE_SIZE
     if state.settings.disable_destination_sampling or (
@@ -163,7 +163,7 @@ def _od_sample(
     ):
         # FIXME interaction_sample will return unsampled complete alternatives
         # with probs and pick_count
-        logger.info(
+        logger.debug(
             (
                 "Estimation mode for %s using unsampled alternatives "
                 "short_circuit_choices"
@@ -607,7 +607,7 @@ def od_presample(
     trace_label = tracing.extend_trace_label(trace_label, "presample")
     chunk_tag = "tour_od.presample"
 
-    logger.info(f"{trace_label} od_presample")
+    logger.debug(f"{trace_label} od_presample")
 
     alt_od_col_name = get_od_id_col(ORIG_MAZ, DEST_TAZ)
 
@@ -711,7 +711,7 @@ def run_od_sample(
         )
 
     if pre_sample_taz:
-        logger.info(
+        logger.debug(
             "Running %s destination_presample with %d tours" % (trace_label, len(tours))
         )
 
@@ -780,7 +780,7 @@ def run_od_logsums(
         choosers[origin_id_col].astype(str) + "_" + choosers[dest_id_col].astype(str)
     )
 
-    logger.info("Running %s with %s rows", trace_label, len(choosers))
+    logger.debug("Running %s with %s rows", trace_label, len(choosers))
 
     state.tracing.dump_df(DUMP, choosers, trace_label, "choosers")
 
@@ -989,7 +989,7 @@ def run_od_simulate(
 
     constants = model_settings.CONSTANTS
 
-    logger.info("Running tour_destination_simulate with %d persons", len(choosers))
+    logger.debug("Running tour_destination_simulate with %d persons", len(choosers))
 
     # create wrapper with keys for this lookup - in this case there is an origin ID
     # column and a destination ID columns in the alternatives table.
diff --git a/activitysim/abm/models/util/tour_scheduling.py b/activitysim/abm/models/util/tour_scheduling.py
index 0a7c6675d..80474db59 100644
--- a/activitysim/abm/models/util/tour_scheduling.py
+++ b/activitysim/abm/models/util/tour_scheduling.py
@@ -140,7 +140,7 @@ def run_tour_scheduling(
     if estimators:
         timetable.begin_transaction(list(estimators.values()))
 
-    logger.info(f"Running {trace_label} with %d tours", len(chooser_tours))
+    logger.debug(f"Running {trace_label} with %d tours", len(chooser_tours))
     choices = vts.vectorize_tour_scheduling(
         state,
         chooser_tours,
diff --git a/activitysim/abm/models/util/vectorize_tour_scheduling.py b/activitysim/abm/models/util/vectorize_tour_scheduling.py
index d4593c21f..85efdaa70 100644
--- a/activitysim/abm/models/util/vectorize_tour_scheduling.py
+++ b/activitysim/abm/models/util/vectorize_tour_scheduling.py
@@ -161,7 +161,7 @@ def _compute_logsums(
             mandatory=False,
         )
         choosers = alt_tdd.join(tours_merged, how="left", rsuffix="_chooser")
-        logger.info(
+        logger.debug(
             f"{trace_label} compute_logsums for {choosers.shape[0]} choosers {alt_tdd.shape[0]} alts"
         )
 
@@ -194,7 +194,7 @@ def _compute_logsums(
 
         if preprocessor_settings:
             simulate.set_skim_wrapper_targets(choosers, skims)
-            logger.info(
+            logger.debug(
                 f"{trace_label} start preprocessing prior to compute_logsums for {choosers.shape[0]} choosers {alt_tdd.shape[0]} alts"
             )
             expressions.assign_columns(
@@ -204,7 +204,7 @@ def _compute_logsums(
                 locals_dict=locals_dict,
                 trace_label=trace_label,
             )
-            logger.info(
+            logger.debug(
                 f"{trace_label} end preprocessing prior to compute_logsums for {choosers.shape[0]} choosers {alt_tdd.shape[0]} alts"
             )
 
@@ -426,7 +426,7 @@ def compute_tour_scheduling_logsums(
         )
         chunk_sizer.log_df(trace_label, "deduped_alt_tdds", deduped_alt_tdds)
 
-        logger.info(
+        logger.debug(
             f"{trace_label} compute_logsums "
             f"deduped_alt_tdds reduced number of rows by "
             f"{round(100 * (len(alt_tdd) - len(deduped_alt_tdds)) / len(alt_tdd), 2)}% "
@@ -758,7 +758,7 @@ def _schedule_tours(
 
     """
 
-    logger.info(
+    logger.debug(
         "%s schedule_tours running %d tour choices" % (tour_trace_label, len(tours))
     )
 
@@ -908,7 +908,7 @@ def schedule_tours(
         logger.info("schedule_tours %s tours not monotonic_increasing - sorting df")
         tours = tours.sort_index()
 
-    logger.info(
+    logger.debug(
         "%s schedule_tours running %d tour choices" % (tour_trace_label, len(tours))
     )
 
diff --git a/activitysim/core/assign.py b/activitysim/core/assign.py
index 870904db6..fe39bfc44 100644
--- a/activitysim/core/assign.py
+++ b/activitysim/core/assign.py
@@ -360,7 +360,7 @@ def rng_lognormal(random_draws, mu, sigma, broadcast=True, scale=False):
             )
 
         if trace_label:
-            logger.info(f"{trace_label}.assign_variables {target} = {expression}")
+            logger.debug(f"{trace_label}.assign_variables {target} = {expression}")
 
         if is_temp_singular(target) or is_throwaway(target):
             try:
diff --git a/activitysim/core/chunk.py b/activitysim/core/chunk.py
index c7a3d4d7a..a9064ca40 100644
--- a/activitysim/core/chunk.py
+++ b/activitysim/core/chunk.py
@@ -1224,7 +1224,7 @@ def adaptive_chunked_choosers(
         # The adaptive chunking logic is expensive and sometimes results
         # in needless data copying.  So we short circuit it entirely
         # when chunking is disabled.
-        logger.info(f"Running chunkless with {len(choosers)} choosers")
+        logger.debug(f"Running chunkless with {len(choosers)} choosers")
         yield 0, choosers, trace_label, ChunkSizer(
             state, "chunkless", trace_label, 0, 0, state.settings.chunk_training_mode
         )
@@ -1250,7 +1250,7 @@ def adaptive_chunked_choosers(
     assert num_choosers > 0
     assert chunk_size >= 0
 
-    logger.info(
+    logger.debug(
         f"{trace_label} Running adaptive_chunked_choosers with {num_choosers} choosers"
     )
 
@@ -1275,7 +1275,7 @@ def adaptive_chunked_choosers(
             # grab the next chunk based on current rows_per_chunk
             chooser_chunk = choosers[offset : offset + rows_per_chunk]
 
-            logger.info(
+            logger.debug(
                 f"Running chunk {i} of {estimated_number_of_chunks or '?'} "
                 f"with {len(chooser_chunk)} of {num_choosers} choosers"
             )
@@ -1342,7 +1342,7 @@ def adaptive_chunked_choosers_and_alts(
         # The adaptive chunking logic is expensive and sometimes results
         # in needless data copying.  So we short circuit it entirely
         # when chunking is disabled.
-        logger.info(f"Running chunkless with {len(choosers)} choosers")
+        logger.debug(f"Running chunkless with {len(choosers)} choosers")
         chunk_sizer = ChunkSizer(
             state, "chunkless", trace_label, 0, 0, state.settings.chunk_training_mode
         )
@@ -1372,7 +1372,7 @@ def adaptive_chunked_choosers_and_alts(
             alternatives.index[last_repeat]
         )
 
-    logger.info(
+    logger.debug(
         f"{trace_label} Running adaptive_chunked_choosers_and_alts "
         f"with {num_choosers} choosers and {num_alternatives} alternatives"
     )
@@ -1442,7 +1442,7 @@ def adaptive_chunked_choosers_and_alts(
                     chooser_chunk.index == np.unique(alternative_chunk.index.values)
                 ).all()
 
-            logger.info(
+            logger.debug(
                 f"Running chunk {i} of {estimated_number_of_chunks or '?'} "
                 f"with {len(chooser_chunk)} of {num_choosers} choosers"
             )
@@ -1481,7 +1481,7 @@ def adaptive_chunked_choosers_by_chunk_id(
         # The adaptive chunking logic is expensive and sometimes results
         # in needless data copying.  So we short circuit it entirely
         # when chunking is disabled.
-        logger.info(f"Running chunkless with {len(choosers)} choosers")
+        logger.debug(f"Running chunkless with {len(choosers)} choosers")
         chunk_sizer = ChunkSizer(
             state, "chunkless", trace_label, 0, 0, state.settings.chunk_training_mode
         )
@@ -1519,7 +1519,7 @@ def adaptive_chunked_choosers_by_chunk_id(
                 choosers["chunk_id"].between(offset, offset + rows_per_chunk - 1)
             ]
 
-            logger.info(
+            logger.debug(
                 f"{trace_label} Running chunk {i} of {estimated_number_of_chunks or '?'} "
                 f"with {rows_per_chunk} of {num_choosers} choosers"
             )
diff --git a/activitysim/core/flow.py b/activitysim/core/flow.py
index d3e83e72b..9bfff5816 100644
--- a/activitysim/core/flow.py
+++ b/activitysim/core/flow.py
@@ -77,7 +77,7 @@ def mark(self, tag, ping=True, logger=None, suffix=""):
             self._time_log.append((tag, timedelta(seconds=elapsed)))
             self._time_point = now
             if logger is not None:
-                logger.info(
+                logger.debug(
                     "elapsed time {0} {1} {2}".format(
                         tag,
                         timedelta(seconds=elapsed),
@@ -94,7 +94,7 @@ def mark(self, tag, ping=True, logger=None, suffix=""):
         else:
             self.aggregate_timing[tag] += elapsed
 
-    def summary(self, logger, tag, level=20, suffix=None):
+    def summary(self, logger, tag, level=10, suffix=None):
         gross_elaspsed = time.time() - self._time_start
         if suffix:
             msg = f"{tag} in {timedelta(seconds=gross_elaspsed)}: ({suffix})\n"
diff --git a/activitysim/core/interaction_sample_simulate.py b/activitysim/core/interaction_sample_simulate.py
index 9cdea3292..8704674b5 100644
--- a/activitysim/core/interaction_sample_simulate.py
+++ b/activitysim/core/interaction_sample_simulate.py
@@ -133,7 +133,7 @@ def _interaction_sample_simulate(
 
     # assert alternatives.index.name == choosers.index.name
     # asserting the index names are the same tells us nothing about the underlying data so why?
-    logger.info(
+    logger.debug(
         f"{trace_label} start merging choosers and alternatives to create interaction_df"
     )
 
@@ -169,7 +169,7 @@ def _interaction_sample_simulate(
         )
 
     interaction_df = alternatives.join(choosers, how="left", rsuffix="_chooser")
-    logger.info(
+    logger.debug(
         f"{trace_label} end merging choosers and alternatives to create interaction_df"
     )
 
diff --git a/activitysim/core/interaction_simulate.py b/activitysim/core/interaction_simulate.py
index d0af58e77..0473a5809 100644
--- a/activitysim/core/interaction_simulate.py
+++ b/activitysim/core/interaction_simulate.py
@@ -83,7 +83,7 @@ def eval_interaction_utilities(
     start_time = time.time()
 
     trace_label = tracing.extend_trace_label(trace_label, "eval_interaction_utils")
-    logger.info("Running eval_interaction_utilities on %s rows" % df.shape[0])
+    logger.debug("Running eval_interaction_utilities on %s rows" % df.shape[0])
 
     sharrow_enabled = state.settings.sharrow
     if compute_settings is None:
@@ -91,7 +91,7 @@ def eval_interaction_utilities(
     if compute_settings.sharrow_skip:
         sharrow_enabled = False
 
-    logger.info(f"{trace_label} sharrow_enabled is {sharrow_enabled}")
+    logger.debug(f"{trace_label} sharrow_enabled is {sharrow_enabled}")
 
     trace_eval_results = None
 
@@ -633,11 +633,11 @@ def to_series(x):
                         raise  # enter debugger now to see what's up
             timelogger.mark("sharrow interact test", True, logger, trace_label)
 
-    logger.info(f"utilities.dtypes {trace_label}\n{utilities.dtypes}")
+    logger.debug(f"utilities.dtypes {trace_label}\n{utilities.dtypes}")
     end_time = time.time()
 
     timelogger.summary(logger, "TIMING interact_simulate.eval_utils")
-    logger.info(
+    logger.debug(
         f"interact_simulate.eval_utils runtime: {timedelta(seconds=end_time - start_time)} {trace_label}"
     )
 
diff --git a/activitysim/core/simulate.py b/activitysim/core/simulate.py
index 6cd4a51f6..fe22ca9ec 100644
--- a/activitysim/core/simulate.py
+++ b/activitysim/core/simulate.py
@@ -861,7 +861,7 @@ def eval_utilities(
     chunk_sizer.log_df(trace_label, "utilities", None)
 
     end_time = time.time()
-    logger.info(
+    logger.debug(
         f"simulate.eval_utils runtime: {timedelta(seconds=end_time - start_time)} {trace_label}"
     )
     timelogger.summary(logger, "simulate.eval_utils timing")
diff --git a/activitysim/core/tracing.py b/activitysim/core/tracing.py
index 88bf0fc16..8c9fb61bd 100644
--- a/activitysim/core/tracing.py
+++ b/activitysim/core/tracing.py
@@ -147,12 +147,12 @@ def print_summary(label, df, describe=False, value_counts=False):
 
     if value_counts:
         n = 10
-        logger.info(
+        logger.debug(
             "%s top %s value counts:\n%s" % (label, n, df.value_counts().nlargest(n))
         )
 
     if describe:
-        logger.info("%s summary:\n%s" % (label, df.describe()))
+        logger.debug("%s summary:\n%s" % (label, df.describe()))
 
 
 def write_df_csv(
diff --git a/activitysim/core/util.py b/activitysim/core/util.py
index 429d3eee0..aeb09ae16 100644
--- a/activitysim/core/util.py
+++ b/activitysim/core/util.py
@@ -722,9 +722,9 @@ def drop_unused_columns(
         custom_chooser_lines = inspect.getsource(custom_chooser)
         unique_variables_in_spec.update(re.findall(pattern, custom_chooser_lines))
 
-    logger.info("Dropping unused variables in chooser table")
+    logger.debug("Dropping unused variables in chooser table")
 
-    logger.info(
+    logger.debug(
         "before dropping, the choosers table has {} columns: {}".format(
             len(choosers.columns), choosers.columns
         )
@@ -733,7 +733,7 @@ def drop_unused_columns(
     # keep only variables needed for spec
     choosers = choosers[[c for c in choosers.columns if c in unique_variables_in_spec]]
 
-    logger.info(
+    logger.debug(
         "after dropping, the choosers table has {} columns: {}".format(
             len(choosers.columns), choosers.columns
         )
diff --git a/docs/dev-guide/logging.md b/docs/dev-guide/logging.md
index f2a5a3db7..b00451c46 100644
--- a/docs/dev-guide/logging.md
+++ b/docs/dev-guide/logging.md
@@ -69,3 +69,67 @@ handlers:
       if_sub_task: WARNING
       if_not_sub_task: NOTSET
 ```
+
+## Logging levels
+Python's built-in `logging` module that includes five levels of logging, which are (in order
+of increasing severity): `DEBUG`, `INFO`, `WARNING`, `ERROR`, and `CRITICAL`. One can set the
+minimum level to display messages in both the console window as well as the output logfile
+within `logging.yaml` in the model settings. For example, if the block of code below were
+inside the `logging.yaml` file, than the console window and output activitysim.log file would
+print every logging message at the level of `INFO` and above:
+
+```yaml
+loggers:
+  activitysim:
+    level: INFO
+    handlers: [console, logfile]
+    propogate: false
+```
+
+However, if a model run were to crash and the user wanted to print all of the `DEBUG` messages
+in order to diagnose what was causing the crash, they would need to change the `level` within
+the logging settings:
+
+```yaml
+loggers:
+  activitysim:
+    level: DEBUG
+    handlers: [console, logfile]
+    propogate: false
+```
+
+The following guidelines demonstrate how each level is used within ActivitySim:
+
+### Debug (Level 10)
+The `DEBUG` message indicates detailed information that would be of interest to a user while
+debugging a model. The information reported at this level can include:
+- Runtimes of specific steps of model components, such as the time to run each of sampling,
+  logsum computation, and simulation in destination choice
+- Table attributes at various stages of processing, such as the size or columns
+- Evaluations of preprocessor or specification expressions
+- General repetitive messages that can be used to narrow down exactly where an error is occuring
+
+### Info (Level 20)
+The `INFO` message gives reports general information about how the status of the model run,
+particularly where in the model flow the system is at. The information reported at this level
+can include:
+- Beginning and ending of a model step
+- Intermediate stages of a longer step. For example, in trip destination, the trip number and
+  segment will be reported at this level.
+
+### Warning (Level 30)
+The `WARNING` message notifies the user of a potential issue that they should be aware of,
+but doesn't result in the model system failing. The information reported at this level can include:
+- Future changes to dependencies
+- ActivitySim needing to force certain travel behavior due to such behavior not working
+
+### Error (Level 40)
+The `ERROR` message gives the user information that is causing an error in a model step. The
+information reported at this level can include:
+- More detailed issues on what could be causing an error message that wouldn't be shown in the
+  traceback message
+
+## Critical (Level 50)
+The `CRITICAL` message gives the user information that is causing a critical error in a model step.
+The information reported at this level can include:
+- Reporting to the user on the teardown of a subprocess
\ No newline at end of file