adding parameter for price perception

matsim-scenarios · Jul 25, 2024 · ee25196 · ee25196
1 parent 315fab3
commit ee25196
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Showing 2 changed files with 12 additions and 3 deletions.
diff --git a/src/main/python/choicemodels/estimate_biogeme_plan_choice.py b/src/main/python/choicemodels/estimate_biogeme_plan_choice.py
@@ -23,6 +23,7 @@
     parser.add_argument("--est-performing", help="Estimate the beta for performing", action="store_true")
     parser.add_argument("--est-exp-income", help="Estimate exponent for income", action="store_true")
     parser.add_argument("--est-util-money", help="Estimate utility of money", action="store_true")
+    parser.add_argument("--est-price-perception", help="Estimate price perception", action="store_true")
     parser.add_argument("--ascs", help="Predefined ASCs", nargs="+", action='append', default=[])
     parser.add_argument("--car-util", help="Fixed utility for car", type=float, default=None)
     parser.add_argument("--no-mxl", help="Disable mixed logit", action="store_true")
@@ -61,6 +62,7 @@
     UTIL_MONEY = Beta('UTIL_MONEY', 1, 0, 2, ESTIMATE if args.est_util_money else FIXED)
 
     BETA_PERFORMING = Beta('BETA_PERFORMING', 6.88, 1, 15, ESTIMATE if args.est_performing else FIXED)
+    BETA_PRICE_PERCEPTION = Beta('BETA_FIXED_PRICE_PERCEPTION', 1, 0, 1, ESTIMATE if args.est_price_perception else FIXED)
 
     for mode in ds.modes:
 
@@ -95,12 +97,14 @@
 
     for i in range(1, ds.k + 1):
         # Price is already negative
-        u = v[f"plan_{i}_price"] * UTIL_MONEY * (1 if args.no_income else (ds.global_income / v["income"]) ** EXP_INCOME)
+
+        perceived_price = BETA_PRICE_PERCEPTION * v[f"plan_{i}_fixed_price"] + v[f"plan_{i}_distance_price"]
+
+        u = perceived_price * UTIL_MONEY * (1 if args.no_income else (ds.global_income / v["income"]) ** EXP_INCOME)
         u -= v[f"plan_{i}_pt_n_switches"]
 
         for mode in ds.modes:
             u += ASC[mode] * v[f"plan_{i}_{mode}_usage"]
-
             u += -BETA_PERFORMING * v[f"plan_{i}_{mode}_hours"] * (2 if mode == "ride" else 1)
 
         u += v[f"plan_{i}_car_used"] * B_CAR
@@ -127,6 +131,8 @@
         modelName += "_fixed_ascs"
     if args.no_income:
         modelName += "_no_income"
+    if args.est_price_perception:
+        modelName += "_price_perception"
 
     biogeme.modelName = modelName
     biogeme.weight = v["weight"]

diff --git a/src/main/python/choicemodels/prepare.py b/src/main/python/choicemodels/prepare.py
@@ -45,7 +45,6 @@ def read_plan_choices(input_file: str, sample: float = 1, seed: int = 42) -> Pla
     if sample < 1:
         df_wide = df_wide.sample(frac=sample, random_state=seed)
 
-    print("Modes:", modes)
     print("Number of choices:", len(df_wide))
 
     df_wide['custom_id'] = np.arange(len(df_wide))  # Add unique identifier
@@ -84,6 +83,8 @@ def calc_plan_variables(df, k, modes, use_util_money=False, add_util_performing=
 
         # Price is only monetary costs
         df[f"plan_{i}_price"] = 0
+        df[f"plan_{i}_fixed_price"] = 0
+        df[f"plan_{i}_distance_price"] = 0
 
         # Costs will also include time costs
         df[f"plan_{i}_utils"] = 0
@@ -97,6 +98,8 @@ def calc_plan_variables(df, k, modes, use_util_money=False, add_util_performing=
 
             df[f"plan_{i}_{mode}_fixed_cost"] = fixed_costs
             df[f"plan_{i}_price"] += fixed_costs + distance_costs
+            df[f"plan_{i}_fixed_price"] += fixed_costs
+            df[f"plan_{i}_distance_price"] += distance_costs
 
             df[f"plan_{i}_{mode}_used"] = (df[f"plan_{i}_{mode}_usage"] > 0) * 1
             df[f"plan_{i}_tt_hours"] -= df[f"plan_{i}_{mode}_hours"]