MAL 2023 Car ownership

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)

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:

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)

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