Allow zero probs option in pnr lot choice

activitysim/abm/models/park_and_ride_lot_choice.py

@@ -82,6 +82,9 @@ class ParkAndRideLotChoiceSettings(LogitComponentSettings, extra="forbid"):
     TRACE_PNR_CAPACITIES_PER_ITERATION: bool = True
     """If True, output park-and-ride lot occupancy at each iteration to the trace folder."""
 
+    ALLOW_ZERO_PROBS: bool = False
+    """If True, return -1 when a chooser has no lot with positive probability."""
+
 
 def filter_chooser_to_transit_accessible_destinations(
     state: workflow.State,
@@ -206,6 +209,12 @@ def run_park_and_ride_lot_choice(
     pnr_alts = land_use[land_use[model_settings.LANDUSE_PNR_SPACES_COLUMN] > 0]
     pnr_alts["pnr_zone_id"] = pnr_alts.index.values
 
+    if pnr_alts.empty and model_settings.ALLOW_ZERO_PROBS:
+        logger.info(
+            "No park-and-ride lots are available. Returning -1 for all choosers."
+        )
+        return pd.Series(data=-1, index=choosers.index)
+
     # if we are running with capacitated pnr lots, we need to flag the lots that are over-capacitated
     if pnr_capacity_cls is not None:
         pnr_alts["pnr_lot_full"] = pnr_capacity_cls.flag_capacitated_pnr_zones(pnr_alts)
@@ -349,6 +358,8 @@ def run_park_and_ride_lot_choice(
         estimator=estimator,
         explicit_chunk_size=model_settings.explicit_chunk,
         compute_settings=model_settings.compute_settings,
+        allow_zero_probs=model_settings.ALLOW_ZERO_PROBS,
+        zero_prob_choice_val=-1,
     )
 
     choices = choices.reindex(choosers.index, fill_value=-1)

activitysim/abm/models/util/test/test_park_and_ride_models.py

@@ -158,6 +158,7 @@ def model_settings_base():
         compute_settings=None,
         preprocessor=_EmptyPreprocessor(),
         alts_preprocessor=None,
+        ALLOW_ZERO_PROBS=False,
     )
 
 
@@ -329,6 +330,75 @@ def test_run_pnr_handles_non_unique_index(state, network_los, model_settings_bas
     assert (choices == 1).all()
 
 
+@pytest.mark.parametrize("use_explicit_error_terms", [False, True])
+def test_run_pnr_allows_zero_probabilities(
+    state, network_los, model_settings_base, use_explicit_error_terms
+):
+    """Return -1 only for choosers with no available lot."""
+    model_settings_base.ALLOW_ZERO_PROBS = True
+    state.settings.use_explicit_error_terms = use_explicit_error_terms
+    land_use = pd.DataFrame(
+        {"pnr_spaces": [10, 10]}, index=pd.Index([1, 2], name="zone_id")
+    )
+    choosers = pd.DataFrame(
+        {
+            "destination": [1, 5, 2],
+            "home_zone_id": [3, 3, 3],
+            "in_period": [1, 1, 1],
+        },
+        index=pd.Index([100, 100, 102], name="tour_id"),
+    )
+    _write_model_files(
+        state,
+        "pnr_zero_prob_spec.csv",
+        """
+Description,Expression,coefficient
+availability,@-1000 * (df.destination != df.pnr_zone_id),coef_one
+""",
+        "pnr_zero_prob_coeffs.csv",
+        """
+coefficient_name,value,constrain
+coef_one,1.0,F
+""",
+    )
+    model_settings_base.SPEC = "pnr_zero_prob_spec.csv"
+    model_settings_base.COEFFICIENTS = "pnr_zero_prob_coeffs.csv"
+
+    state.get_rn_generator().begin_step("test_run_pnr_allows_zero_probabilities")
+    choices = pnr_lot_choice.run_park_and_ride_lot_choice(
+        state=state,
+        choosers=choosers,
+        land_use=land_use,
+        network_los=network_los,
+        model_settings=model_settings_base,
+    )
+    state.get_rn_generator().end_step("test_run_pnr_allows_zero_probabilities")
+
+    assert choices.index.tolist() == [100, 100, 102]
+    assert choices.tolist() == [1, -1, 2]
+
+
+def test_run_pnr_allows_no_lots(state, network_los, model_settings_base):
+    model_settings_base.ALLOW_ZERO_PROBS = True
+    land_use = pd.DataFrame(
+        {"pnr_spaces": [0, 0]}, index=pd.Index([1, 2], name="zone_id")
+    )
+    choosers = pd.DataFrame(
+        {"destination": [1], "home_zone_id": [3]},
+        index=pd.Index([100], name="tour_id"),
+    )
+
+    choices = pnr_lot_choice.run_park_and_ride_lot_choice(
+        state=state,
+        choosers=choosers,
+        land_use=land_use,
+        network_los=network_los,
+        model_settings=model_settings_base,
+    )
+
+    assert choices.to_dict() == {100: -1}
+
+
 def test_run_pnr_calculates_drive_transit_and_capacity_utilities(
     state, network_los, model_settings_base
 ):

activitysim/core/interaction_simulate.py

@@ -662,6 +662,8 @@ def _interaction_simulate(
     estimator=None,
     chunk_sizer=None,
     compute_settings: ComputeSettings | None = None,
+    allow_zero_probs=False,
+    zero_prob_choice_val=None,
 ):
     """
     Run a MNL simulation in the situation in which alternatives must
@@ -915,8 +917,19 @@ def _interaction_simulate(
 
     if state.settings.use_explicit_error_terms:
         utilities = logit.validate_utils(
-            state, utilities, trace_label=trace_label, trace_choosers=choosers
+            state,
+            utilities,
+            allow_zero_probs=allow_zero_probs,
+            trace_label=trace_label,
+            trace_choosers=choosers,
         )
+        if allow_zero_probs:
+            zero_probs = (
+                utilities.sum(axis=1) <= utilities.shape[1] * logit.UTIL_UNAVAILABLE
+            )
+            if zero_probs.any():
+                # Give unavailable rows a temporary choice before replacing it below.
+                utilities.loc[zero_probs, 0] = logit.UTIL_LARGE_ENOUGH
         positions, rands = logit.make_choices_utility_based(
             state, utilities, trace_label=trace_label, trace_choosers=choosers
         )
@@ -928,7 +941,12 @@ def _interaction_simulate(
         # convert to probabilities (utilities exponentiated and normalized to probs)
         # probs is same shape as utilities, one row per chooser and one column for alternative
         probs = logit.utils_to_probs(
-            state, utilities, trace_label=trace_label, trace_choosers=choosers
+            state,
+            utilities,
+            allow_zero_probs=allow_zero_probs,
+            trace_label=trace_label,
+            trace_choosers=choosers,
+            overflow_protection=not allow_zero_probs,
         )
         chunk_sizer.log_df(trace_label, "probs", probs)
 
@@ -942,6 +960,12 @@ def _interaction_simulate(
                 column_labels=["alternative", "probability"],
             )
 
+        if allow_zero_probs:
+            zero_probs = probs.sum(axis=1) == 0
+            if zero_probs.any():
+                # Give unavailable rows a temporary choice before replacing it below.
+                probs.loc[zero_probs, 0] = 1.0
+
         # make choices
         # positions is series with the chosen alternative represented as a column index in probs
         # which is an integer between zero and num alternatives in the alternative sample
@@ -962,6 +986,8 @@ def _interaction_simulate(
 
     # create a series with index from choosers and the index of the chosen alternative
     choices = pd.Series(choices, index=choosers.index)
+    if allow_zero_probs and zero_probs.any() and zero_prob_choice_val is not None:
+        choices.loc[zero_probs] = zero_prob_choice_val
     chunk_sizer.log_df(trace_label, "choices", choices)
 
     if have_trace_targets:
@@ -993,6 +1019,8 @@ def interaction_simulate(
     estimator=None,
     explicit_chunk_size=0,
     compute_settings: ComputeSettings | None = None,
+    allow_zero_probs=False,
+    zero_prob_choice_val=None,
 ):
     """
     Run a simulation in the situation in which alternatives must
@@ -1035,6 +1063,10 @@ def interaction_simulate(
     explicit_chunk_size : float, optional
         If > 0, specifies the chunk size to use when chunking the interaction
         simulation. If < 1, specifies the fraction of the total number of choosers.
+    allow_zero_probs : bool, optional
+        Accept choosers for which every alternative has zero probability.
+    zero_prob_choice_val : optional
+        Choice value to return for those choosers when allow_zero_probs is True.
 
     Returns
     -------
@@ -1070,6 +1102,8 @@ def interaction_simulate(
             estimator=estimator,
             chunk_sizer=chunk_sizer,
             compute_settings=compute_settings,
+            allow_zero_probs=allow_zero_probs,
+            zero_prob_choice_val=zero_prob_choice_val,
         )
 
         result_list.append(choices)

docs/dev-guide/components/park_and_ride_lot_choice.md

@@ -52,6 +52,7 @@ names must be supplied for the implementing model.
    COEFFICIENTS: park_and_ride_lot_choice_coefficients.csv
    LANDUSE_PNR_SPACES_COLUMN: pnr_spaces
    LANDUSE_COL_FOR_PNR_ELIGIBLE_DEST: pnr_eligible_destination
+   ALLOW_ZERO_PROBS: true
    ITERATE_WITH_TOUR_MODE_CHOICE: false
    ```
 
@@ -76,6 +77,11 @@ This is a destination screen, so the utility specification must still handle
 unavailable paths for individual lot/destination pairs. Filtered-out tours
 receive `pnr_zone_id = -1`.
 
+Set `ALLOW_ZERO_PROBS: true` to return `pnr_zone_id = -1` when a tour has no
+lot with positive choice probability, including when no zones have positive
+park-and-ride spaces. The default is `false`. The mode-choice specification
+must make park-and-ride modes unavailable for tours with `pnr_zone_id == -1`.
+
 ### Skims and Utility Expressions
 
 The following wrappers are available in lot choice and, when `pnr_zone_id` is