HSLdevcom · johpiip · Apr 19, 2021 · Feb 4, 2022 · Feb 4, 2022 · Feb 4, 2022
diff --git a/Scripts/assignment/emme_assignment.py b/Scripts/assignment/emme_assignment.py
@@ -172,6 +172,8 @@ def aggregate_results(self, resultdata):
             linktypes.add(param.roadtypes[linktype])
         linklengths = pandas.Series(0.0, linktypes)
         soft_modes = param.transit_classes + ("bike",)
+        attr_names = self.day_scenario.attributes("LINK")
+        resultdata.print_line("Link\t" + "\t".join(attr_names), "links")
         network = self.day_scenario.get_network()
         for link in network.links():
             linktype = link.type % 100
@@ -197,6 +199,10 @@ def aggregate_results(self, resultdata):
                 linklengths[param.railtypes[linktype]] += link.length
             else:
                 linklengths[param.roadtypes[vdf]] += link.length / 2
+            wkt = "LINESTRING ({} {}, {} {})".format(
+                link.i_node.x, link.i_node.y, link.j_node.x, link.j_node.y)
+            attrs = "\t".join([str(link[attr]) for attr in attr_names])
+            resultdata.print_line(wkt + "\t" + attrs, "links")
         if faulty_kela_code_nodes:
             s = "Municipality KELA code not found for nodes: " + ", ".join(
                 faulty_kela_code_nodes)

diff --git a/Scripts/assignment/emme_bindings/mock_project.py b/Scripts/assignment/emme_bindings/mock_project.py
@@ -420,6 +420,11 @@ def __init__(self, idx):
     def zone_numbers(self):
         return sorted(self._network._centroids)
 
+    def attributes(self, attr_type):
+        network = self.get_network()
+        # TODO Return other attributes except extra attributes
+        return list(network._extra_attr[attr_type])
+
     def extra_attribute(self, idx):
         network = self.get_network()
         for attr_type in network._extra_attr:

diff --git a/Scripts/helmet.py b/Scripts/helmet.py
@@ -85,6 +85,9 @@ def main(args):
             results_path, ass_model, args.scenario_name)
     log_extra["status"]["results"] = model.mode_share
 
+    model.cdm.set_car_growth(constant=args.car_growth_constant,
+                             factor=args.car_growth_factor)
+
     # Run traffic assignment simulation for N iterations,
     # on last iteration model-system will save the results
     log_extra["status"]["state"] = "preparing"
@@ -247,6 +250,17 @@ def main(args):
         action="store_true",
         default=config.USE_FIXED_TRANSIT_COST,
         help="Using this flag activates use of pre-calculated (fixed) transit costs."),
+    # MAL 2023 input data
+    parser.add_argument(
+        "--car-growth-constant",
+        type=float,
+        default=0.0,
+        help="Car ownership growth constant. To increase, try 0.1."),
+    parser.add_argument(
+        "--car-growth-factor",
+        type=float,
+        default=1.0,
+        help="Car ownership growth factor. To decrease, try 0.8."),
     args = parser.parse_args()
 
     log.initialize(args)

diff --git a/Scripts/models/linear.py b/Scripts/models/linear.py
@@ -80,10 +80,24 @@ def __init__(self, zone_data_base, zone_data_forecast, bounds, resultdata):
             out=numpy.array(forecast_sh_detached), where=pop_growth!=0)
         self.zone_data._values["share_detached_houses_new"] = pandas.Series(
             share_detached_new, self.zone_data.zone_numbers[self.bounds])
-
+        self.set_car_growth()
+
+    def set_car_growth(self, constant=0.0, factor=1.0):
+        """Set extra car ownership growth for sensitivity analyses.
+
+        Parameters
+        ----------
+        constant : float (optional)
+            Constant to add to prediction
+        factor : float (optional)
+            Factor to multiply prediction by
+        """
+        self._growth_constant = constant
+        self._growth_factor = factor
+
     def predict(self):
         """Get car ownership prediction for zones.
-        
+
         Return
         ------
         pandas.Series
@@ -108,6 +122,8 @@ def predict(self):
                             .clip(upper=1.0))
         prediction = (self.pop_growth_share * prediction
                       + (1-self.pop_growth_share) * base_car_density)
+        prediction += self._growth_constant
+        prediction *= self._growth_factor
         self.print_results(prediction)
         return prediction
 

diff --git a/Scripts/parameters/assignment.py b/Scripts/parameters/assignment.py
@@ -31,6 +31,7 @@
     40: RoadClass("collector", "any", 5, 900, 36, 0.833),
     41: RoadClass("local", "any", 5, 600, 30, 1.000),
     42: RoadClass("local", "any", 5, 500, 23, 1.304),
+    43: RoadClass("collector", "any", 5, 750, 30, 0.833),
 }
 connector_link_types = (84, 85, 86, 87, 88, 98, 99)
 connector = RoadClass("connector", "any", 99, 0, 0, 0)

diff --git a/Scripts/parameters/tour_generation.py b/Scripts/parameters/tour_generation.py
@@ -9,12 +9,16 @@
     3: 1.05446538,
     4: 1.2455917 + 0.1043963,
 }
+MUULITAR_CONSTANT_ZERO = 0.0
+MUULITAR_CONSTANT_HW = -0.3
+MUULITAR_CONSTANT_HO = 0.38
+MUULITAR_CONSTANT_HU = -0.08
 # Tour combinations (calibrated)
 tour_combinations = {
 # utility function 1
     0: {
         () : {
-            "constant":   0.000000000,
+            "constant":   0.000000000 + MUULITAR_CONSTANT_ZERO,
             "individual_dummy": {
                 "age_50-64": -0.305509545 ,
                 "age_65-99":  0.597976527
@@ -25,7 +29,7 @@
     },
     1: {
         ("hw",) : {
-            "constant":   0.000000000 + 0.0210,
+            "constant":   0.000000000 + 0.0210 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  2.306249018 + 0.1065,
                 "age_30-49":  2.977241136 - 0.3498,
@@ -48,7 +52,7 @@
         },
 # utility function 4
         ("hu",) : {
-            "constant":   0.000000000 + 0.3000,
+            "constant":   0.000000000 + 0.3000 + MUULITAR_CONSTANT_HU,
             "individual_dummy": {
                 "age_18-29":  0.000000000 + 0.0653,
                 "age_30-49": -1.586979829 - 0.0192,
@@ -76,7 +80,7 @@
         },
 # utility function 6
         ("ho",) : {
-            "constant":   0.811674639,
+            "constant":   0.811674639 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_7-17":   0.000000000 - 0.1096,
                 "age_18-29":  0.000000000 + 0.0679,
@@ -92,7 +96,7 @@
     },
     2: {
         ("hw", "hw") : {
-            "constant":  -6.702389265,
+            "constant":  -6.702389265 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  2.306249018 - 1.0022,
                 "age_30-49":  2.977241136 + 0.3275,
@@ -106,7 +110,7 @@
         },
 # utility function 8
         ("hw", "hu") : {
-            "constant":  -8.418852173 + 0.2000,
+            "constant":  -8.418852173 + 0.2000 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  2.306249018 - 0.4439,
                 "age_30-49": -1.586979829 +  2.977241136 + 0.4961,
@@ -121,7 +125,7 @@
         },
 # utility function 9
         ("hw", "hs") : {
-            "constant":  -5.468303413,
+            "constant":  -5.468303413 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  0.632156675 +  2.306249018 - 0.1900,
                 "age_30-49":  1.106558979 +  2.977241136 + 0.0878,
@@ -135,7 +139,7 @@
         },
 # utility function 10
         ("hw", "ho") : {
-            "constant":  -3.969665707,
+            "constant":  -3.969665707 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  2.306249018 + 0.0229,
                 "age_30-49":  2.977241136 + 0.0059,
@@ -182,7 +186,7 @@
         },
 # utility function 14
         ("hu", "hs") : {
-            "constant":  -5.264912587 + 0.0736,
+            "constant":  -5.264912587 + 0.0736 + MUULITAR_CONSTANT_HU,
             "individual_dummy": {
                 "age_18-29":  0.632156675 - 0.0197,
                 "age_30-49":  1.106558979  -1.586979829 - 0.6757,
@@ -197,7 +201,7 @@
         },
 # utility function 15
         ("hu", "ho") : {
-            "constant":  -4.133565561 + 0.0834,
+            "constant":  -4.133565561 + 0.0834 + MUULITAR_CONSTANT_HU,
             "individual_dummy": {
                 "age_18-29":  0.000000000 + 0.2038,
                 "age_30-49": -1.586979829 - 0.8545,
@@ -226,7 +230,7 @@
         },
 # utility function 17
         ("hs", "ho") : {
-            "constant":  -3.615413138,
+            "constant":  -3.615413138 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_7-17":   0.000000000 + 0.1376,
                 "age_18-29":  0.632156675 + 0.0695,
@@ -241,7 +245,7 @@
         },
 # utility function 18
         ("ho", "ho") : {
-            "constant":  -2.954069138,
+            "constant":  -2.954069138 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_7-17":   0.000000000 + 0.5035,
                 "age_18-29":  0.000000000 - 0.1393,
@@ -258,7 +262,7 @@
     },
     3: {
         ("hw", "hw", "ho") : {
-            "constant":  -7.640316015,
+            "constant":  -7.640316015 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  2.306249018 ,
                 "age_30-49":  2.977241136 - 0.4304,
@@ -270,7 +274,7 @@
         },
 # utility function 20
         ("hw", "hs", "hs") : {
-            "constant":  -6.996908123,
+            "constant":  -6.996908123 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  0.632156675 +  2.306249018 - 0.7910,
                 "age_30-49":  1.106558979 +  2.977241136 + 0.4528,
@@ -282,7 +286,7 @@
         },
 # utility function 21
         ("hw", "hs", "ho") : {
-            "constant":  -6.280857590,
+            "constant":  -6.280857590 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  0.632156675 +  2.306249018 + 0.2580,
                 "age_30-49":  1.106558979 +  2.977241136 + 0.1582,
@@ -294,7 +298,7 @@
         },
 # utility function 22
         ("hw", "ho", "ho") : {
-            "constant":  -5.143814369,
+            "constant":  -5.143814369 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  2.306249018 - 0.2782,
                 "age_30-49":  2.977241136 + 0.3222,
@@ -331,7 +335,7 @@
         },
 # utility function 25
         ("hu", "hs", "ho") : {
-            "constant": -11.751808160,
+            "constant": -11.751808160 + MUULITAR_CONSTANT_HU,
             "individual_dummy": {
                 "age_18-29":  0.632156675 + 0.1437,
                 "age_30-49":  1.106558979  -1.586979829 + 0.8652,
@@ -346,7 +350,7 @@
         },
 # utility function 26
         ("hu", "ho", "ho") : {
-            "constant": -11.342729830,
+            "constant": -11.342729830 + MUULITAR_CONSTANT_HU,
             "individual_dummy": {
                 "age_18-29": -0.000000000 + 0.1541,
                 "age_30-49": -1.586979829 + 0.5275,
@@ -375,7 +379,7 @@
         },
 # utility function 28
         ("hs", "hs", "ho") : {
-            "constant":  -4.709369964,
+            "constant":  -4.709369964 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_18-29":  0.632156675 - 0.7508,
                 "age_30-49":  1.106558979 + 0.5842,
@@ -389,7 +393,7 @@
         },
 # utility function 29
         ("hs", "ho", "ho") : {
-            "constant":  -4.115616267,
+            "constant":  -4.115616267 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_18-29":  0.632156675 - 0.1442,
                 "age_30-49":  1.106558979 ,
@@ -403,7 +407,7 @@
         },
 # utility function 30
         ("ho", "ho", "ho") : {
-            "constant":  -4.110394781,
+            "constant":  -4.110394781 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_30-49":  0.000000000 - 0.1750,
                 "age_50-64":  0.000000000 + 0.1126,
@@ -418,7 +422,7 @@
     },
     4: {
         ("hw", "hs", "hs", "ho") : {
-            "constant":  -8.782904966,
+            "constant":  -8.782904966 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  0.632156675 +  2.306249018 ,
                 "age_30-49":  1.106558979 +  2.977241136 + 0.2190,
@@ -432,7 +436,7 @@
         },
 # utility function 32
         ("hw", "hs", "ho", "ho") : {
-            "constant":  -7.819600775,
+            "constant":  -7.819600775 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  0.632156675 +  2.306249018 + 0.5615,
                 "age_30-49":  1.106558979 +  2.977241136 + 0.2939,
@@ -446,7 +450,7 @@
         },
 # utility function 33
         ("hw", "ho", "ho", "ho") : {
-            "constant":  -6.323991971,
+            "constant":  -6.323991971 + MUULITAR_CONSTANT_HW,
             "individual_dummy": {
                 "age_18-29":  2.306249018 + 0.3338,
                 "age_30-49":  2.977241136 ,
@@ -474,7 +478,7 @@
         },
 # utility function 35
         ("hs", "hs", "hs", "ho") : {
-            "constant":  -6.280534875,
+            "constant":  -6.280534875 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_18-29":  0.632156675 - 1.3263,
                 "age_30-49":  1.106558979 + 0.9876,
@@ -488,7 +492,7 @@
         },
 # utility function 36
         ("hs", "hs", "ho", "ho") : {
-            "constant":  -5.728407971,
+            "constant":  -5.728407971 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_18-29":  0.632156675 - 0.8239,
                 "age_30-49":  1.106558979 + 0.4522,
@@ -502,7 +506,7 @@
         },
 # utility function 37
         ("hs", "ho", "ho", "ho") : {
-            "constant":  -5.167664200,
+            "constant":  -5.167664200 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_18-29":  0.632156675 - 0.8511,
                 "age_30-49":  1.106558979 + 0.7283,
@@ -516,7 +520,7 @@
         },
 # utility function 38
         ("ho", "ho", "ho", "ho") : {
-            "constant":  -4.892323651,
+            "constant":  -4.892323651 + MUULITAR_CONSTANT_HO,
             "individual_dummy": {
                 "age_18-29":  0.000000000 - 2.0113,
                 "age_30-49":  0.000000000 + 0.2214,

diff --git a/Scripts/tests/integration/test_models.py b/Scripts/tests/integration/test_models.py
@@ -53,7 +53,8 @@ def test_models(self):
         self._validate_impedances(impedance["iht"])
 
         # Check that model result does not change
-        self.assertAlmostEquals(model.mode_share[0]["car"], 0.22489513375983478)
+        # when using Muulitar parameters
+        self.assertAlmostEquals(model.mode_share[0]["car"], 0.223537259813515)
 
         print("Model system test done")