Merge pull request #68 from Urban-Analytics-Technology-Platform/fix_w…

…orkzone_assignment

Fix workzone assignment

scripts/3.1_assign_primary_feasible_zones.py

@@ -96,7 +96,9 @@ def main(config_file):
         # If travel_times is not true or loading failed, create a new travel time matrix
         logger.info("No travel time matrix found. Creating a new travel time matrix.")
         # Create a new travel time matrix based on distances between zones
-        travel_times = zones_to_time_matrix(zones=boundaries, id_col=config.zone_id)
+        travel_times = zones_to_time_matrix(
+            zones=boundaries, id_col=config.zone_id, time_units="m"
+        )
         logger.info("Travel time estimates created")
         # save travel_times as parquet
 

src/acbm/assigning/feasible_zones_primary.py

@@ -134,7 +134,9 @@ def get_possible_zones(
 
     if travel_times is None:
         logger.info("Travel time matrix not provided: Creating travel times estimates")
-        travel_times = zones_to_time_matrix(zones=boundaries, id_col=zone_id)
+        travel_times = zones_to_time_matrix(
+            zones=boundaries, id_col=zone_id, time_units="m"
+        )
 
     list_of_modes = activity_chains["mode"].unique()
     print(f"Unique modes found in the dataset are: {list_of_modes}")

src/acbm/assigning/plots.py

@@ -200,7 +200,7 @@ def plot_workzone_assignment_heatmap(
         )
 
         ax = sns.heatmap(
-            heatmap_data, cmap="RdBu", ax=axes[i], cbar=i == len(categories) - 1
+            heatmap_data, cmap="viridis", ax=axes[i], cbar=i == len(categories) - 1
         )
         axes[i].set_title(f"Demand Difference: % of {category} Total")
         axes[i].set_xlabel("Home Zone")

src/acbm/assigning/select_zone_work.py

@@ -488,16 +488,13 @@ def select_work_zone_optimization(
                 for person_id, origins in self.activities_to_assign.items()
                 if (person_id, from_zone, to_zone) in assignment_vars
             )
-            # Calculate the assigned percentage based on the total flows to each destination zone
-            if to_zone in self.total_flows:
-                total_people = self.total_flows[to_zone]
+            # Calculate the assigned percentage based on the total flows from each origin zone
+            if from_zone in self.total_flows:
+                total_people = self.total_flows[from_zone]
                 assigned_percentage = assigned_flow / total_people
-            # If the origin zone is not in the total flows, set the assigned percentage to 0
             else:
                 assigned_percentage = 0
-                logger.warning(
-                    f"Warning: Destination {to_zone} not found in total_flows."
-                )
+                logger.warning(f"Warning: Origin {from_zone} not found in total_flows.")
 
             if use_percentages:
                 # to satisfy both constraints, abs_dev will be a positive number (it is the larger of the two)

src/acbm/assigning/utils.py

@@ -286,6 +286,7 @@ def _get_activities_per_zone_df(activities_per_zone: dict) -> pd.DataFrame:
 
 def zones_to_time_matrix(
     zones: gpd.GeoDataFrame,
+    time_units: str,
     id_col: Optional[str] = None,
 ) -> pd.DataFrame:
     """
@@ -302,7 +303,8 @@ def zones_to_time_matrix(
         A GeoDataFrame containing the zones.
     id_col: str, optional
         The name of the column in the zones GeoDataFrame to use as the ID. If None, the index values are used. Default is None.
-
+    time_units: str, optional
+        The units to use for the travel time. Options are 's' for seconds and 'm' for minutes.
     Returns
     -------
     pd.DataFrame
@@ -347,6 +349,10 @@ def zones_to_time_matrix(
         mode_data["time"] = mode_data["distance"] / speed
         long_format_data.append(mode_data)
 
+        # Convert time to the desired units
+        if time_units == "m":
+            mode_data["time"] = mode_data["time"] / 60  # Convert seconds to minutes
+
     # Concatenate the list into a single DataFrame
     return pd.concat(long_format_data, ignore_index=True)