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matsim-scenarios · Jul 26, 2024 · 1f1dd1a · 1f1dd1a
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Showing 4 changed files with 33 additions and 27 deletions.
diff --git a/input/v6.3/params/bg_plan_based_v1.yaml b/input/v6.3/params/bg_plan_based_v1.yaml
@@ -22,24 +22,24 @@ scoring:
           constant: 0
         - mode: car
           constant: 0
-          dailyMonetaryConstant: -4.627
+          dailyMonetaryConstant: -5.1038273
         - mode: pt
-          constant: -0.724590
+          constant: 0.065940
         - mode: bike
-          constant: -1.590161
+          constant: -1.758609
         - mode: ride
-          constant: -2.150079
+          constant: -2.194756
 advancedScoring:
   incomeExponent: 0.549347
   scoringParameters:
     - subpopulation: person
       modeParams:
         - mode: bike
-          deltaConstant: 1.284915
+          deltaConstant: 1.401864
           varConstant: normal
         - mode: pt
-          deltaConstant: 2.177996
+          deltaConstant: 2.061751
           varConstant: normal
         - mode: ride
-          deltaConstant: 1.565783
+          deltaConstant: 1.534127
           varConstant: normal
diff --git a/input/v6.3/params/bg_trip_based_v1.yaml b/input/v6.3/params/bg_trip_based_v1.yaml
@@ -1,19 +1,20 @@
-#  + python -u estimate_biogeme_trip_choice.py --input trip-choices.csv --est-exp-income --est-performing
+#  + python -u estimate_biogeme_plan_choice.py --input plan-choices-diverse_9.csv --performing 7.547802 --exp-income 0.549347 --est-price-perception
 #  Modes:  ['walk', 'pt', 'car', 'bike', 'ride']
-#  Number of choices:  27235
-#  Varying: ['km', 'hours', 'walking_km', 'switches', 'valid']
-#    Results for model trip_choice_performing_exp_income
+#  Number of plans:  8259
+#  Number of choices for plan:  9
+#  Number of choices: 8259
+#    Using MXL modes ['pt', 'bike', 'ride']
+#    Results for model plan-choices-diverse_9_price_perception
 #  Nbr of parameters:		9
-#  Sample size:			8238
-#  Observations:			27235
+#  Sample size:			8259
 #  Excluded data:			0
-#  Null log likelihood:		-40155.25
-#  Final log likelihood:		-22571.32
-#  Likelihood ratio test (null):		35167.85
-#  Rho square (null):			0.438
-#  Rho bar square (null):			0.438
-#  Akaike Information Criterion:	45160.65
-#  Bayesian Information Criterion:	45223.8
+#  Null log likelihood:		-16584.31
+#  Final log likelihood:		-16922.1
+#  Likelihood ratio test (null):		-675.5779
+#  Rho square (null):			-0.0204
+#  Rho bar square (null):			-0.0209
+#  Akaike Information Criterion:	33862.19
+#  Bayesian Information Criterion:	33925.37
 scoring:
   scoringParameters:
     - performing: 7.547802
@@ -22,7 +23,7 @@ scoring:
           constant: 0
         - mode: car
           constant: 0
-          dailyMonetaryConstant: -4.627
+          dailyMonetaryConstant: -5.1038273
         - mode: pt
           constant: -2.554736
         - mode: bike

diff --git a/src/main/python/choicemodels/estimate_biogeme_plan_choice.py b/src/main/python/choicemodels/estimate_biogeme_plan_choice.py
@@ -64,7 +64,7 @@
     UTIL_MONEY = Beta('UTIL_MONEY', 1, 0, 2, ESTIMATE if args.est_util_money else FIXED)
 
     BETA_PERFORMING = Beta('BETA_PERFORMING', args.performing, 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)
+    BETA_PRICE_PERCEPTION = Beta('BETA_CAR_PRICE_PERCEPTION', 1, 0, 1, ESTIMATE if args.est_price_perception else FIXED)
 
     is_est_car = "car" in args.mxl_modes
 
@@ -108,7 +108,7 @@
     for i in range(1, ds.k + 1):
         # Price is already negative
 
-        perceived_price = BETA_PRICE_PERCEPTION * v[f"plan_{i}_fixed_price"] + v[f"plan_{i}_distance_price"]
+        perceived_price = BETA_PRICE_PERCEPTION * v[f"plan_{i}_car_price"] + v[f"plan_{i}_non_car_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"]

diff --git a/src/main/python/choicemodels/prepare.py b/src/main/python/choicemodels/prepare.py
@@ -83,8 +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
+        df[f"plan_{i}_car_price"] = 0
+        df[f"plan_{i}_non_car_price"] = 0
 
         # Costs will also include time costs
         df[f"plan_{i}_utils"] = 0
@@ -98,8 +98,13 @@ 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
+
+            if mode == "car":
+                df[f"plan_{i}_car_price"] += fixed_costs
+            else:
+                df[f"plan_{i}_non_car_price"] += fixed_costs
+
+            df[f"plan_{i}_non_car_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"]